{ "site": { "name": "AI Model Index", "tagline": "The transparency layer for the AI model economy", "description": "Independent, reproducible rankings, recipe definitions, and live intelligence for every frontier AI model." }, "common": { "loading": "Loading…", "error": "Something went wrong", "retry": "Retry", "cancel": "Cancel", "close": "Close", "save": "Save", "copy": "Copy", "copied": "Copied to clipboard", "copyLink": "Copy link", "copyJson": "Copy as JSON", "search": "Search", "filter": "Filter", "clear": "Clear", "clearFilters": "Clear filters", "apply": "Apply", "submit": "Submit", "back": "Back", "next": "Next", "previous": "Previous", "open": "Open", "share": "Share", "view": "View", "seeMore": "See more", "seeAll": "See all", "readMore": "Read more", "lastUpdated": "Last updated {{time}}", "comingSoon": "Coming soon", "yes": "Yes", "no": "No", "all": "All", "none": "None", "or": "or", "and": "and", "with": "with", "from": "from", "to": "to", "of": "of", "by": "by", "in": "in", "rank": "Rank", "score": "Score", "models": "models", "model": "model", "modelsCount_one": "{{count}} model", "modelsCount_other": "{{count}} models", "variantsCount_one": "{{count}} variant", "variantsCount_other": "{{count}} variants", "componentsCount_one": "{{count}} component", "componentsCount_other": "{{count}} components", "coverage": "Coverage", "trust": "Trust", "freshness": "Freshness", "policies": "Policies", "policy": "Policy", "type": "Type", "direction": "Direction", "higherIsBetter": "Higher is better", "lowerIsBetter": "Lower is better", "home": "Home" }, "nav": { "home": "Home", "models": "Models", "compare": "Compare", "explore": "Explore", "indexes": "Indexes", "recipes": "Recipes", "benchmarks": "Benchmarks", "sources": "Sources", "coverage": "Coverage", "methodology": "Methodology", "controversies": "Controversies", "articles": "Articles", "ask": "Ask", "timeline": "Timeline", "labs": "Labs", "dataQuality": "Data Quality", "api": "API", "embed": "Embed", "acknowledgments": "Acknowledgments", "partners": "Partners", "changelog": "Changelog", "docs": "Documentation", "glossary": "Glossary", "calculations": "Calculations", "language": "Language", "selectLanguage": "Select language", "flagship": "Flagship", "domain": "Domain", "domainIndexes": "Domain Indexes", "interestingPairs": "Interesting Pairs", "sourceLeaderboards": "Source Leaderboards", "exploreModels": "Explore Models", "generativeMedia": "Generative Media", "compareMulti": "Multi-model", "savedComparisons": "Saved", "coreNavigation": "Core", "leaderboards": "Leaderboards", "adminConsole": "Admin", "openMenu": "Open menu", "closeMenu": "Close menu", "rssFeeds": "RSS Feeds", "snapshots": "Snapshots", "exploreIndexes": "Browse all indexes", "exploreRecipes": "Browse all recipes", "roadmap": "What we're building next", "press": "Press kit, fact sheet, and media coverage", "doctor": "Find the right model for your use case", "quarterly": "Quarterly deep dive", "firstLooks": "New model first looks", "locales": "Multilingual coverage", "bestModels": "Best models", "forRoles": "For your role", "pricing": "Pricing & plans", "security": "Security & trust", "status": "System status", "jobs": "Careers", "about": "About us", "brand": "Brand assets", "trends": "Trends", "hallOfFame": "Hall of Fame", "risingStars": "Rising Stars", "fallingGiants": "Falling Giants", "openSourceHero": "Open-source Heroes", "correlations": "Correlations", "partnerPortal": "Partners", "analytics": "Analytics", "resources": "Resources", "platform": "Platform", "blog": "Blog" }, "domainIndexes": { "title": "Domain Indexes", "metaTitle": "Domain Indexes — AI Model Index", "metaDescription": "Specialized composite indexes for specific domains: YouTube, Scientific, Local, Multilingual, Healthcare, Legal, Finance.", "ogDescription": "Specialized indexes for specific domains." }, "home": { "hero": { "titlePrefix": "The reconciliation layer for", "titleRotating": [ "transparent", "auditable", "data-driven", "lab-agnostic", "composable" ], "titleSuffix": "and model leaderboards", "subtitle": "Artificial Analysis, Vals AI, LMArena, LLM Stats, official benchmark leaderboards, and provider data—one auditable evidence layer. Compare consolidated indexes, then inspect the source-level benchmark rows behind them.", "methodologyCta": "Methodology", "browseCta": "Open Consolidated Index", "compareCta": "Explore Benchmarks", "dataUpdated": "Data updated {{relative}}", "titleAccent": "AI benchmarks", "scoringSystemCta": "How Reconciliation Works" }, "topModels": "Top Models", "indexesHeading": "Core Indexes", "stats": { "indexes": "Indexes", "models": "Models", "families": "Benchmark Families", "variants": "Variants" }, "specialtyHeading": "Specialty & Domain Indexes", "specialtyCard": { "leader": "Leader", "clickToExplore": "Click to explore", "seed": "Seed" }, "livePulse": "Live Pulse", "livePulseSubtitle": "Latest index intelligence", "seeControversies": "See controversies", "articlesHeading": "Articles & Reports", "articles": { "weekly": { "title": "Weekly Report", "subtitle": "Biggest score movers, new models, and current index leaders.", "cta": "Read report" }, "topModels": { "title": "Top Models", "subtitle": "Ranked analysis of the top 10 models in the Overall Frontier Capability index.", "cta": "Read article" }, "comparison": { "title": "Model Comparison", "subtitle": "Head-to-head data-driven analysis of leading models.", "cta": "Read comparison" }, "lab": { "title": "Lab Spotlight", "subtitle": "Complete analysis of a lab’s model lineup, scores, and rankings.", "cta": "Read spotlight" } }, "newsletter": { "badge": "Weekly Insights", "heading": "Stay ahead of the curve in AI model capabilities", "body": "Subscribe to our weekly digest. We analyze raw data from 8 leading benchmark families, filter out the noise, and send you the key updates directly to your inbox." }, "howScoringWorks": "How the scoring works", "ingestRaw": "Ingest raw benchmarks", "normalizeWeight": "Normalize & weight", "auditEveryPoint": "Audit every point", "ingestDesc": "Every score traces back to a raw benchmark row from trusted sources like Artificial Analysis, HuggingFace, EQ-Bench, and LMSYS. No black boxes.", "normalizeDesc": "Raw scores are normalized to a 0–100 scale. Each index applies transparent weights to benchmark families and variants. Policies like downweighting and provisional scoring are documented per contribution.", "auditDesc": "Click any score to see the full breakdown: which benchmarks contributed, what raw values were used, what policies were applied, and how many points each contributed. Export as JSON or CSV.", "readScoringDocs": "Read the full scoring system documentation", "compositeScore": "Composite score", "rankOf": "Rank #{{rank}} of {{total}}+" }, "models": { "title": "Models", "subtitle": "Search and compare models across indexes.", "searchPlaceholder": "Search models or labs...", "profile": "Profile", "noResults": "No models found.", "table": { "model": "Model", "lab": "Lab" } }, "model": { "detailsHeading": "Model Details", "lab": "Lab", "context": "Context", "tokens": "tokens", "toolSupport": "Tool Support", "pricing": "Pricing", "per1M": "per 1M", "promptCompletion": "prompt / completion", "share": "Share", "copied": "Copied!", "compareWith": "Compare with...", "compare": "Compare", "controversyHeading": "Statistical Divergence Warning", "controversyBody": "Benchmark controversy checks detected inconsistent scoring behavior for this model relative to cluster consensus:", "scoreHistory": "Score History", "loadScoreHistory": "Load score history", "scoreHistoryOnDemand": "Score history loads only when requested to keep model pages fast and reduce expensive database work.", "scoreHistoryLoading": "Loading score history…", "scoreHistoryEmpty": "No multi-snapshot score history is published for this model yet.", "scoreHistoryUnavailable": "Score history is temporarily unavailable. No absence claim has been inferred from this failed request.", "retryScoreHistory": "Try again", "sinceFirstSnapshot": "since first snapshot", "rank": "Rank #{{rank}}", "rankShort": "rank {{rank}}", "coverageConfidence": "Coverage {{coverage}}% · Confidence {{confidence}}%", "estBand": "Est. Band: {{low}} - {{high}}", "lowCoverage": "Low coverage ({{coverage}}%). Score may change significantly as more sources are added.", "multilingualBanner": { "title": "Top {{rank}} in Multilingual / Arabic", "body": "Strong on Flores-200 translation and XQuAD multilingual QA — score {{score}}/100.", "cta": "View full leaderboard" }, "noScores": "No index scores available for this model.", "benchmarkResults": "Benchmark Results", "allFamilies": "All families ({{count}})", "noResults": "No results available.", "noFamilyResults": "No results for the selected family.", "fullIndexRankings": "Full Index Rankings", "viewLeaderboard": "View leaderboard", "table": { "benchmark": "Benchmark", "family": "Family", "scoreType": "Score Type", "raw": "Raw Value", "normalized": "Normalized", "confidence": "Confidence", "measured": "Measured" } }, "indexes": { "title": "Indexes", "subtitle": "Composite indexes in the AI Model Index. Each one is a published recipe with a defined formula, a freshness policy, and a coverage threshold.", "viewRecipe": "View recipe", "viewLeaderboard": "View leaderboard", "totalRecipes": "Total recipes", "activeRecipes": "Active recipes", "minCoverage": "Min coverage {{pct}}", "bySlug": { "default-consolidated": { "title": "Default Consolidated", "subtitle": "Practical baseline index combining Artificial Analysis, Vals AI, LLM Stats, LMArena, and EQ-Bench." }, "overall-frontier-capability": { "title": "Overall Frontier Capability", "subtitle": "Pure capability flagship. No cost, speed, provider availability, or pricing constraints." }, "practical-best-model": { "title": "Practical Best Model", "subtitle": "Balanced index weighting frontier capability, pricing, speed, safety, and provider availability." }, "best-for-startups-builders": { "title": "Best for Startups & Builders", "subtitle": "Optimized for building, coding, fast prototyping, app generation, and production cost efficiency." }, "frontier-coding": { "title": "Frontier Coding", "subtitle": "Main coding capability recipe across repository work, dev environment, algorithms, and technical code." }, "agentic-work": { "title": "Agentic Work", "subtitle": "Evaluates tool usage, coding agents, planning, and multi-turn execution tasks." }, "knowledge-factuality": { "title": "Knowledge & Factuality", "subtitle": "Factual recall, hallucination mitigation, and general world knowledge." }, "scientific-research-math": { "title": "Scientific / Research / Math", "subtitle": "Graduate-level logic, reasoning, and advanced mathematical problem solving." }, "multimodal-document": { "title": "Multimodal / Document", "subtitle": "Comprehends complex PDF documents, visual layout logic, charts, and diagrams." }, "generative-media": { "title": "Generative Media", "subtitle": "Composite evaluation of image, video, and audio/voice generation systems." }, "best-local-open": { "title": "Best Local & Open-Weight", "subtitle": "Leaderboard for open models focused on resource efficiency, local coding, and offline reasoning." }, "best-for-healthcare": { "title": "Best for Healthcare & Life Sciences", "subtitle": "Composite for clinical and biomedical workloads: factual reliability, harm refusal, long-context document handling, and multilingual patient-facing communication." }, "best-for-legal": { "title": "Best for Legal & Compliance", "subtitle": "Composite for legal workflows: long-context document review, precise instruction following, multilingual cross-jurisdictional drafting, and refusal of unauthorized advice." }, "best-for-finance": { "title": "Best for Finance & Quantitative Work", "subtitle": "Composite for financial workflows: precise math, tabular/document reasoning, agentic multi-step analysis, and refusal of unauthorized advice." }, "voice-streaming": { "title": "Best for Voice & Streaming", "subtitle": "Real-time voice assistants, streaming TTS, voice cloning, and low-latency conversational deployments. Audio quality, low TTFB, multilingual speech, and cost-per-minute at scale." }, "arabic-silma": { "title": "Arabic Language Quality (SILMA)", "subtitle": "Classical + dialectal Arabic reading comprehension, translation in/out of Arabic, and cross-lingual XQuAD-style QA. Heavily weighted to Arabic-specific sources." }, "open-weights-tracker": { "title": "Open-Weights Tracker", "subtitle": "Ranks open-weight model families (Llama, Mistral, Qwen, DeepSeek, Gemma, Phi) on raw capability parity with closed frontier, plus Reddit /r/LocalLlama community sentiment." } } }, "index": { "notFound": "Index not found.", "public": "Public", "private": "Private", "version": "Version {{version}}", "weightEditor": "\"What-If\" Weight Editor", "customizingWeights": "Customizing Weights...", "weightEditorHelp": "Drag the sliders to test custom priority formulas. Sliders auto-compensate to sum to exactly 100%.", "copiedLink": "Copied Link", "shareConfig": "Share Config", "resetDefaults": "Reset Defaults", "demoHeading": "Demo / Placeholder Data", "demoBody": "This index is showing synthetic demo data because real benchmark sources have not yet been ingested for all of its components. Rankings and scores will change significantly once live data is connected.", "avgCoverage": "Average coverage:", "avgCoverageSuffix": "(top model: {{model}} — {{coverage}}% coverage)", "defaultPct": "(default {{weight}}%)", "totalWeight": "Total Combined Weight", "componentWeightsRef": "Component Weights Reference", "table": { "component": "Component", "weight": "Weight", "description": "Description", "variants": "Source Variants" }, "allLabs": "All labs ({{count}})", "allCoverage": "All coverage", "showComponents": "Show component columns", "showing": "Showing {{shown}} of {{total}}", "leaderboard": "Leaderboard", "recalculatedActive": "Recalculated rankings active", "sourceVariantsUsed": "Source Variants Used", "trustPct": "Trust {{pct}}%", "readMethodology": "Read Methodology" }, "recipes": { "title": "Recipe Registry", "subtitle": "Every recipe in the AI Model Index, with its level, status, and the full list of weighted components.", "viewDetails": "Open in drawer", "public": "Public", "draft": "Draft", "planned": "Planned", "active": "Active", "noRecipes": "No recipes registered yet.", "composition": "Recipe Composition", "compositionSubtitle": "How each top-level recipe decomposes into domain and primitive sub-recipes, and which raw signals each one ultimately aggregates. Click any node to open it in the drawer.", "liveIndexScores": "Live Index Scores (Runtime View)", "liveIndexScoresSubtitle": "See the exact composite weight distributions and formula dependencies registered for all 12 leaderboard indexes.", "availableIndexes": "Available Indexes ({{count}})", "formulaComponents": "Formula Components & Weighted Benchmark Signals", "selectIndexPrompt": "Select an index from the list to view its recipe weights.", "heroPill": "Use-Case Recommended Recipes", "pageTitle": "Model Selection Recipes", "pageSubtitle": "Find empirical, ELO-backed model recommendations tailored to your budget and hardware setup. Explore the exact index weight recipes powering our leaderboards.", "runComparison": "Run Custom Comparison", "statTotal": "Total Recipes", "statTopIndexes": "Top Indexes", "statDomains": "Domains", "statPrimitives": "Primitives", "statFamilies": "Source Families", "searchPlaceholder": "Search recipes, components, or descriptions… (press / to focus)", "noMatch": "No recipes match your search", "empty": "No recipes yet", "noVariants": "No variants assigned to this component." }, "recipeDiff": { "title": "Recipe Diff", "subtitle": "Compare any two recipes side by side. See which components are shared with weight deltas, which are exclusive, and how scoring changes affect the top of the model leaderboard.", "left": "Left (A)", "right": "Right (B)", "swap": "Swap", "subRecipeDepth": "Sub-recipe depth", "depthTop": "top index", "depthDomain": "domain index", "depthPrimitive": "primitive index", "depthUseCase": "use case recipe", "shared": "Shared components", "sharedSubtitle": "In both recipes, with weight delta (A − B)", "onlyLeft": "Only in A ({{name}})", "onlyRight": "Only in B ({{name}})", "noShared": "No components shared between these two recipes.", "noOnlyLeft": "Every component in A also appears in B.", "noOnlyRight": "Every component in B also appears in A.", "totalSignals": "{{count}} total {{depth}} signals", "topModelShifts": "Top 10 model score shifts", "topModelShiftsSubtitle": "Models ranked by absolute score delta between A and B. Positive = ranks higher in A.", "noShifts": "No live index scores available for these two recipes. The recipes might still be in draft status, or the indexes might not be in the live index registry yet.", "pickTwo": "Pick two recipes to diff.", "topShifts": "Top 10 model score shifts", "topShiftsDesc": "Models ranked by absolute score delta between A and B. Positive = ranks higher in A.", "noLiveScores": "No live index scores available for these two recipes. The recipes might still be in draft status, or the indexes might not be in the live index registry yet." }, "benchmarks": { "title": "Benchmarks", "subtitle": "Browse benchmark families and their variants.", "explore": "Explore", "variantCount_one": "{{count}} variant", "variantCount_other": "{{count}} variants", "benchmarkCategories": { "officialApi": "Official API", "arena": "Arena", "benchmarkSuite": "Benchmark Suite", "agentHarness": "Agent Harness", "creativeEq": "Creative / EQ", "officialDataset": "Official Dataset", "provider": "Provider" } }, "benchmark": { "notFound": "Benchmark family not found.", "filterVariant": "Filter Variant", "allVariants": "All Variants ({{count}})", "noVariants": "No variants for this family.", "sourceLabel": "Source:", "exportCsv": "Export CSV", "exportCsvTitle": "Download results as CSV", "website": "Website", "websiteTitle": "View original benchmark website", "scoreType": "Score Type", "signal": "Signal", "policy": "Policy", "trust": "Trust", "direction": "Direction", "higherBetter": "Higher is Better", "lowerBetter": "Lower is Better", "noResults": "No results available.", "table": { "model": "Model", "lab": "Lab", "raw": "Raw Value", "normalized": "Normalized", "percentile": "Percentile", "rank": "Rank", "confidence": "Confidence", "measured": "Measured" } }, "sources": { "title": "Sources", "subtitle": "Public source list with status, policy, trust, and freshness.", "table": { "source": "Source", "policy": "Policy", "trust": "Trust", "freshness": "Freshness", "variants": "Variants" }, "weightsHeading": "Weighted vs Display-only", "weightedBody": "sources contribute directly to composite scores. Their normalized results are multiplied by effective weights and summed into index totals.", "displayOnlyBody": "sources are shown for transparency but do not affect rankings. They are useful when a benchmark is too new, methodologically uncertain, or pending validation. You can still see the raw results, but they will not move any model up or down in the index.", "andBody": "and", "partialBody": "sources contribute partially. Downweighted is typically used for agent+model signals inside pure-model indexes. Provisional is used for early-stage sources that have not yet passed full validation." }, "source": { "backToSources": "All Sources", "editorialRole": "Editorial role", "variants": "Variants ({{count}})", "topModels": "Top models using this family", "topModelsSubtitle": "Average normalized score across the {{count}} variants in this family, weighted by component weight. Only models with at least 20% variant coverage are shown.", "ingestedFrom": "What gets ingested from this family", "unknownFamily": "Unknown source family: {{slug}}", "unknownFamilyDesc": "We could not find a benchmark family with that slug. Try the full source list.", "variantsLabel": "Variants", "trustLabel": "Trust {{value}}%", "policiesLabel": "Policies", "signalTypesLabel": "Signal types" }, "partners": { "title": "Partner with AI Model Index", "subtitle": "Datasets, integrations, and distribution channels that power the world's most transparent AI model leaderboard. Free for academic and open-data partners." }, "controversies": { "title": "Controversies & Live Intelligence", "subtitle": "Significant model movement, leader changes, milestones, and benchmark controversies from the last 30 days.", "filters": "Filters", "type": "Type", "severity": "Severity", "pendingReview": "Pending review", "all": "All", "noEvents": "No controversies match your filters.", "noEventsAny": "No controversies recorded in the last 30 days.", "viewIndex": "View index", "viewModel": "View model", "leaderboardHeading": "Most-Contested Models", "leaderboardSub": "Severity-weighted · last 90 days" }, "events": { "type_big_mover": "Big Mover", "type_new_leader": "New Leader", "type_rising_star": "Rising Star", "type_falling_giant": "Falling Giant", "type_coverage_milestone": "Coverage Milestone", "type_new_model": "New Model", "type_benchmark_controversy": "Benchmark Controversy", "severity_low": "Low", "severity_medium": "Medium", "severity_high": "High", "severity_critical": "Critical", "headline_big_mover": "{{model}} jumped from rank {{from}} to {{to}} in {{index}}", "headline_new_leader": "{{model}} is the new #1 in {{index}}", "headline_rising_star": "{{model}} climbed {{spots}} spots in {{index}}", "headline_falling_giant": "{{model}} dropped {{spots}} spots in {{index}}", "headline_coverage_milestone": "{{model}} now has {{pct}}% coverage in {{index}}", "headline_new_model": "{{model}} joined the {{index}} index", "headline_benchmark_controversy": "Disputed result for {{model}} on {{benchmark}}", "stats": "Stats", "statBigMovers": "Big movers", "statNewLeaders": "New leaders", "statRisingStars": "Rising stars", "statFallingGiants": "Falling giants", "statMilestones": "Milestones", "statNewModels": "New models", "statControversies": "Controversies", "lastDays": "Last 30 days" }, "methodology": { "title": "Methodology", "subtitle": "How the AI Model Index works. The recipe system, freshness policy, confidence model, and verification pipeline.", "intro": "This page explains the methodology behind the AI Model Index, including how scores are normalized, how coverage and confidence are calculated, and how different types of benchmarks are treated.", "deepDives": "Deep dives", "minRead": "min read", "backHome": "Back to home", "viewArticle": "Read the article", "versionHistory": "Version History", "versionCol": "Version", "dateCol": "Date", "changesCol": "Changes", "v21Changes": "Added Terminal-Bench agent/model distinction, updated normalization formula to 70/30 robust/percentile split", "v20Changes": "Initial public release with full recipe system, coverage, and confidence scoring", "v14Changes": "Added domain-specific indexes for Healthcare, Legal, and Finance", "sections": { "incompatible": { "heading": "Raw scores are incompatible", "body1": "Benchmarks use different units, scales, and directions. A 1500 Elo rating on an arena is not directly comparable to an 82% pass rate on a coding suite, or a tokens-per-second latency measurement. Without normalization, combining raw scores into an index is statistically meaningless.", "body2": "Therefore, the index converts every raw result to a normalized 0-100 score using a robust quantitative method before any composition." }, "normalization": { "heading": "Normalization", "body1": "For each variant, we compute the 10th and 90th percentiles of all reported results to establish a robust range. Each raw value is mapped against this range, weighted 70% on robust linear projection and 30% on overall percentile rank.", "body2": "This design resists outliers, automatically handles 'higher is better' and 'lower is better' scales, and preserves score interpretability: 0 means near the worst observed performance, 100 means near the best." }, "coverage": { "heading": "Coverage", "body1": "Coverage measures the proportion of target weight that is actually available for a given model. If a component is missing all its sources, its weight is excluded from the denominator, but coverage drops accordingly.", "body2": "A model with 100% coverage has data for every component. A model with 40% coverage may still be ranked, but the score should be interpreted with caution because large parts of the index are absent." }, "confidence": { "heading": "Confidence", "body1": "Confidence is the product of three factors: source trust, individual result confidence, and coverage. Source trust reflects our assessment of methodological independence and reproducibility. Result confidence is based on sample size, recency, and measurement noise.", "body2": "High confidence does not mean a score is 'correct' in an absolute sense; it means the signal is well-supported by available data. Low confidence means data is sparse, outdated, noisy, or from a less trusted source." }, "policies": { "heading": "Source policies", "intro": "Each variant is assigned a source policy that controls how it contributes to composite scores:", "weightedBody": "Contributes at its full assigned weight. This is the default for verified sources.", "downweightedBody": "Contributes at 35% of its weight. Used for agent+model signals inside pure-model indexes, or for sources with weaker methodology.", "provisionalBody": "Contributes at 25% of its weight while the source is under review or early validation.", "displayOnlyBody": "Displayed for transparency but contributes zero points to composite scores. Useful for pending benchmarks.", "blockedBody": "Explicitly excluded from the index. Results are preserved for audit but never calculated." }, "modelVsAgent": { "heading": "Model vs. Agent distinction", "body1": "Some benchmarks test a pure model (zero-shot or fixed-prompt inference), while others test an agent system (model + tools + scaffolding + prompt engineering). Agent scores can be much higher because the system does more than the model alone.", "body2": "The index classifies each source by signal type. When an agent signal is placed inside a pure capability index, it is downweighted or display-only to keep the composite comparable across models." }, "terminalBench": { "heading": "Terminal-Bench rule", "body1": "Terminal-Bench measures agent+model systems on real-world terminal tasks. Because the benchmark is agent-flavored, its raw results are treated as {{downweighted}} in pure-model indexes, and as {{weighted}} in coding/app-building indexes where agent performance is a legitimate signal of practical value.", "body2": "This policy is not a criticism of Terminal-Bench; it is a category correction so that different types of measurement do not get mixed together." }, "indexWeights": { "heading": "Index weights", "body1": "Each index is a weighted average of its components. Component weights are defined by the index owner and versioned to reproduce historical scores. Scores within a component are aggregated by their variant weights, then multiplied by the component weight to produce contribution points.", "body2": "All weights, policies, normalization coefficients, and source mappings are stored in code and fully auditable from raw result to final number." } }, "seeTerm": "See term in the glossary", "howToRead": { "title": "How to read this page", "body1": "The eight sections below describe, in order, why scores need to be normalized, how we normalize them, what coverage and confidence mean, what our source policies are, and how agent-style benchmarks are treated.", "body2": "Each section ends with a pointer to deeper material — including the worked example in the Calculations page and the source-by-source detail in the Sources page.", "keyTerms": "Key terms" } }, "articles": { "title": "Articles", "subtitle": "Manually-written analysis, guides, and commentary mixed with live data.", "metaDescription": "In-depth analysis, guides, and commentary on AI benchmarks and model capabilities.", "youtubers": { "intro": "YouTubers have a unique AI workload: long-form scriptwriting, tight hook pacing, thumbnails, B-roll, and often voice generation. This article maps our three public indexes — Creative / Writing / EQ, Generative Media, and Best for Creators — to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools by medium", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Creative / Writing / EQ, Generative Media, Multimodal, or Practical Best Model. Coverage and confidence are checked before listing. Click through to any model for the full score breakdown and component-by-component audit trail." }, "tiers": { "free": { "title": "Free / OpenRouter — start with these", "body": "For creators publishing their first videos, these models give you the most capability per token on OpenRouter free tier. Scriptwriting, hook generation, and thumbnail ideation are all reliable." }, "prosumer": { "title": "Prosumer — when quality matters", "body": "For monetized channels and brand work, these are the models that consistently lead the Creative / Writing / EQ and Generative Media indexes. Higher quality, lower repetition, better long-form structure." }, "enterprise": { "title": "Enterprise — production scale", "body": "For agencies and studios running batch content at scale, these are the highest-scoring models on the Practical Best Model index. They are reliable, fast, and come with provider SLAs." } }, "companion": { "image": { "title": "Image generation", "body": "For thumbnails and B-roll stills, midjourney-v6 leads on taste, FLUX leads on prompt adherence, and Stable Diffusion 3 is the most flexible if you self-host." }, "video": { "title": "Video generation", "body": "For B-roll, transitions, and AI-generated cutaways, Sora, Runway Gen-3, and Kling 1.5 are the three to test. Each has its own license and pricing tier." }, "voice": { "title": "Voice & audio", "body": "ElevenLabs leads on naturalness, Suno leads on music, and Udio is closing fast. Use them for narration, music beds, and sound design." }, "seeLeaderboard": "See the leaderboard" } }, "writers": { "intro": "Writers need models that produce clean prose, hold voice across long documents, and respect the difference between a draft and a finished piece. This article maps our Creative / Writing / EQ, Frontier Capability, and Production / Value indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the writing workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Creative / Writing / EQ, Frontier Capability, or Production / Value. Coverage and confidence are checked before listing. Longform writing is sensitive to model behavior across context windows — we explicitly look at how the top variants perform on long inputs, not just short prompts." }, "tiers": { "free": { "title": "Free / OpenRouter — start drafting here", "body": "For drafting blog posts, newsletter copy, and short-form writing, these free OpenRouter models are strong first passes. Expect to do some light editing for voice and rhythm — but the structure and grammar come out clean." }, "prosumer": { "title": "Prosumer — when the prose has to sing", "body": "For long-form essays, fiction, and high-stakes narrative work, these models lead the Creative / Writing / EQ and Frontier Capability indexes. Better longform coherence, lower repetition, stronger voice consistency across chapters." }, "enterprise": { "title": "Enterprise — publishing at scale", "body": "For newsrooms, content marketing teams, and book-length projects, these are the highest-scoring models on the Practical Best Model and Production / Value indexes. Reliable, fast, with strong context windows for long documents." } }, "companion": { "editing": { "title": "Line editing", "body": "Use a second pass with a faster, cheaper model for line-by-line edits: tighten sentences, fix repetition, and normalize voice. The frontier prosumer models can also self-edit — ask for \"the same paragraph with 20% fewer words\" and compare." }, "research": { "title": "Research & fact-check", "body": "Pair your writing model with a search-augmented tool (Perplexity, OpenRouter web plugins, or your own RAG pipeline). Models hallucinate sources and statistics — always verify quotes, numbers, and URLs before publishing." }, "translation": { "title": "Translation & localization", "body": "For publishing in multiple languages, the top OpenRouter multilingual models preserve tone and structure across Arabic, Mandarin, Spanish, Portuguese, French, and German. Always have a native reviewer sign off before going public." }, "seeMethodology": "See our methodology", "seeSources": "See our source coverage", "seeArticles": "See related articles" } }, "coders": { "intro": "Coders have a sharp AI workload: long context across many files, multi-step agentic work, and a low tolerance for hallucinations that break builds. This article maps our Coding & App-Building, Frontier Coding, and Agentic Work indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the coding workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Coding & App-Building, Frontier Coding, or Agentic Work. Coverage and confidence are checked before listing. Agentic results are downweighted by design in pure-model indexes — see our methodology for the full policy." }, "tiers": { "free": { "title": "Free / OpenRouter — pair-program on a budget", "body": "For hobby projects, weekend prototypes, and learning a new language, the free OpenRouter tier gives you surprisingly capable code completions. The frontier free models handle boilerplate, refactors, and unit tests reliably." }, "prosumer": { "title": "Prosumer — daily-driver for shipping code", "body": "For shipping features, debugging, and writing production code, these models lead the Coding & App-Building and Frontier Coding indexes. Strong on long context, multi-file edits, and tool use." }, "enterprise": { "title": "Enterprise — codebases, agents, and review", "body": "For large monorepos, security-sensitive code, and agent-driven workflows, these are the top models on Coding & App-Building with high coverage. Best when paired with Terminal-Bench style agent harness." } }, "companion": { "ide": { "title": "IDE integration", "body": "Pair your model with Cursor, Continue, or your editor of choice. The frontier prosumer models give the best completions when given explicit file paths, function signatures, and recent diffs as context." }, "cli": { "title": "Terminal & agentic tools", "body": "For shell tasks and multi-step coding work, Claude Code, Codex CLI, and Aider are the leading agent harnesses. The Terminal-Bench official leaderboard is the cleanest measure of agent+model performance." }, "review": { "title": "Review & refactor", "body": "Use a second model to review diffs for bugs, security issues, and naming. The top-3 frontier models are all strong reviewers; rotate between them to get diverse feedback on tricky changes." }, "seeRecipes": "See coding recipes", "seeAgentic": "See agentic recipes", "seeMethodology": "See our methodology" } }, "designers": { "intro": "Designers need models that match taste, translate brand voice into copy, and produce high-quality visuals on demand. This article maps our Creative / Writing / EQ, Generative Media, Multimodal, and Frontier Coding indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the design workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Creative / Writing / EQ, Generative Media, Multimodal, or Frontier Coding. Coverage and confidence are checked before listing. Design Arena is currently display-only — see our source notes for the policy." }, "tiers": { "free": { "title": "Free / OpenRouter — concepting and mood boards", "body": "For mood boards, copy variations, and concept ideation, the free OpenRouter models are solid. Pair them with a free-tier image generator for visual exploration before committing to a paid workflow." }, "prosumer": { "title": "Prosumer — copy, layouts, and code-generation", "body": "For shipping landing pages, microcopy, and front-end implementation, these models lead the Creative / Writing / EQ, Multimodal, and Frontier Coding indexes. Strong at translating brand voice into code and copy." }, "enterprise": { "title": "Enterprise — design systems at scale", "body": "For large brand systems, multi-locale product copy, and integrated image/video generation, these are the top models on Generative Media and Creative. Use them for batch content + design system audits." } }, "companion": { "image": { "title": "Image generation", "body": "For hero shots, illustrations, and product imagery: Midjourney v6 leads on taste and composition, FLUX 1.1 Pro leads on prompt adherence, and Stable Diffusion 3 is the most flexible if you self-host." }, "wireframe": { "title": "Wireframes & UI", "body": "For UI generation, Figma AI, Galileo AI, and the Design Arena benchmark family are the leading options. The frontier prosumer models can also produce high-quality HTML/CSS from a wireframe spec." }, "palette": { "title": "Brand & palette", "body": "For brand naming, taglines, and palette generation, the top creative models are remarkably strong. Iterate with the prosumer tier and refine with enterprise when you lock direction." }, "seeLeaderboard": "See the image leaderboard", "seeSources": "See our source coverage", "seeArticles": "See related articles" } }, "researchers": { "intro": "Researchers need models that can ingest long papers, hold math and notation precisely, and reason over multi-step scientific problems. This article maps our Scientific & Research Math, Frontier Capability, and Production / Value indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the research workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Scientific & Research Math, Frontier Capability, or Production / Value. Coverage and confidence are checked before listing. SciCode and similar scientific benchmarks are weighted heavily in this index; see our source notes for the full breakdown." }, "tiers": { "free": { "title": "Free / OpenRouter — literature triage", "body": "For skimming papers, summarizing abstracts, and generating research questions, the free OpenRouter models are strong first passes. They handle long context well, which is essential for ingesting full papers." }, "prosumer": { "title": "Prosumer — research workflows and analysis", "body": "For literature reviews, code-based data analysis, and reasoning over research questions, these models lead the Scientific & Research Math and Frontier Capability indexes. Strong on math, logic, and multi-step reasoning." }, "enterprise": { "title": "Enterprise — long-context scientific work", "body": "For long-context research over full corpora, SciCode-style scientific coding, and complex theorem-proving, these are the top models on Scientific & Research Math and Production / Value. Large context windows, longform coherence, and math rigor." } }, "companion": { "literature": { "title": "Literature search", "body": "For finding, retrieving, and citing papers, pair your model with Semantic Scholar, arXiv, or Perplexity. Always verify the model’s citations against the original source — hallucinated DOIs are common." }, "analysis": { "title": "Data analysis", "body": "For statistical analysis and code generation, the top frontier models handle pandas, scikit-learn, and statsmodels well. Use them for first-pass analysis, but always validate the code before publishing results." }, "verification": { "title": "Verification & reproducibility", "body": "For verifying model claims, the Reproducibility Manifest in our Score Breakdown Drawer shows the exact raw data, normalization parameters, and formula used for any published score. Researchers can audit the math end-to-end." }, "seeSources": "See our source coverage", "seeMethodology": "See our methodology", "seeCalculations": "See worked calculations" } }, "videoCreators": { "intro": "Video creators have a unique AI workload: tight narrative scripts, B-roll generation, voice cloning, and high-volume output. This article maps our Creative / Writing / EQ, Generative Media, and Production / Value indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the video workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Creative / Writing / EQ, Generative Media, or Production / Value. Coverage and confidence are checked before listing. For video workflows we also weight cost efficiency — every minute of generated video adds up." }, "tiers": { "free": { "title": "Free / OpenRouter — script + storyboarding", "body": "For scriptwriting, storyboarding, and shot-list ideation, the free OpenRouter models are reliable. Combine them with a free-tier image generator to mock up frame-by-frame visuals before committing to a video model." }, "prosumer": { "title": "Prosumer — when motion matters", "body": "For short-form ads, social clips, and B-roll generation, these models lead the Generative Media and Creative indexes. Strong narrative, lower repetition, and consistent voice across long scripts." }, "enterprise": { "title": "Enterprise — high-volume video production", "body": "For studios producing at scale — episodic YouTube, ad creative, or training data — these are the top models on Generative Media and Production / Value. High quality, fast iteration, and reliable provider SLAs." } }, "companion": { "videoGen": { "title": "Video generation models", "body": "For text-to-video and image-to-video: Sora 2 leads on realism, Runway Gen-3.5 Alpha Turbo is the most production-friendly, Kling 1.6 is the strongest for stylized clips, and Luma Dream Machine 1.5 is the best open option. Each has its own license and pricing tier." }, "edit": { "title": "Edit & post", "body": "For editing, auto-cuts, and color: Adobe Premiere Pro with Sensei, DaVinci Resolve Studio, and CapCut desktop are the three to test. Each integrates with generative video tools differently." }, "broll": { "title": "B-roll & stock", "body": "For B-roll generation, the top generative video models can produce 4-8 second clips at 1080p+ on the prosumer tier. Use a single model for visual consistency across the cut." }, "seeLeaderboard": "See the video leaderboard", "seeRecipes": "See video recipes", "seeBroll": "See the B-roll guide" } }, "audioCreators": { "intro": "Audio creators need models that can write tight lyrics, hold longform narrative, and produce studio-quality voice and music. This article maps our Creative / Writing / EQ, Generative Media, and Production / Value indexes to three realistic budget tiers. Every recommendation is data-driven, never affiliate-driven.", "integrity": { "title": "Integrity policy", "body": "We only recommend the top-ranked model per capability. We never recommend a lower-performing model because of affiliate commission. If a model is not in the top three on the relevant index, it is not in this article." }, "tiersHeading": "Three budget tiers", "companionHeading": "Companion tools for the audio workflow", "openRouterCta": "Compare on OpenRouter", "dataDriven": { "title": "How we picked these models", "body": "Every model on this page is a top-three finisher on at least one of: Creative / Writing / EQ, Generative Media, or Production / Value. Coverage and confidence are checked before listing. Long audio projects especially benefit from the long-context frontier models — short-context models truncate scripts and lose continuity." }, "tiers": { "free": { "title": "Free / OpenRouter — lyrics, scripts, and prompts", "body": "For song lyrics, podcast outlines, and voice-over scripts, the free OpenRouter models are a strong starting point. Iterate quickly before locking direction, then move to a paid tier for the final cut." }, "prosumer": { "title": "Prosumer — polished production", "body": "For shipping a podcast, EP, or commercial voice-over, these models lead the Creative / Writing / EQ and Generative Media indexes. Better lyrical flow, longer coherence, and stronger adherence to your brief." }, "enterprise": { "title": "Enterprise — studio-grade output at scale", "body": "For label-scale music, audiobook production, or podcast networks, these are the top models on Generative Media and Production / Value. Long context for full scripts, fast iteration, and reliable provider SLAs." } }, "companion": { "tts": { "title": "Voice synthesis", "body": "For TTS and voice cloning: ElevenLabs v3 leads on naturalness and emotional range, Cartesia Sonic is the fastest real-time option, and OpenAI Voice Engine is the easiest API. Each has a license and pricing tier to review." }, "music": { "title": "Music generation", "body": "For music: Suno v4 is the most accessible (text-to-song), Udio is strongest on long-form structure, and Stable Audio 2 leads for sound design. The frontier prosumer LLMs are also great for lyrics + arrangement suggestions." }, "edit": { "title": "Edit & master", "body": "For editing, denoising, and mastering: Adobe Podcast (one-click speech enhancement), iZotope RX 11 (industry-standard repair), and LANDR (automated mastering) are the three to test. Each has a free trial." }, "seeLeaderboard": "See the voice leaderboard", "seeMusic": "See the music leaderboard", "seeSources": "See our source coverage" } }, "autoReports": "Auto-Generated Reports", "topModelsCard": { "title": "Top Models", "subtitle": "Live rankings across all indexes" }, "weeklyCard": { "title": "Weekly Report", "subtitle": "Latest pipeline runs and score changes" }, "allCategories": "All categories", "allTags": "All tags", "showing": "Showing {{shown}} of {{total}}", "byAuthor": "By {{author}}", "noMatch": "No articles match the current filters.", "categories": { "analysis": "Analysis", "guide": "Guide", "news": "News", "opinion": "Opinion" } }, "policies": { "weighted": "Weighted", "downweighted": "Downweighted", "provisional": "Provisional", "display_only": "Display-only", "blocked": "Blocked" }, "levels": { "raw_benchmark": "Raw", "reconciled_signal": "Reconciled", "primitive_index": "Primitive", "domain_index": "Domain", "top_index": "Top Index", "use_case_recipe": "Use Case" }, "freshness": { "current": "Current", "aging": "Aging", "stale": "Stale", "archived": "Archived" }, "signals": { "pure_model": "Pure model", "agent_model": "Agent + model", "provider": "Provider signal", "media": "Generative media", "display": "Display only" }, "footer": { "copyright": "© {{year}} AI Model Index", "madeWith": "Built with the principle that rankings should be reproducible, recipes should be public, and the public deserves to know how each score was made.", "builtBy": "An independent project", "openSource": "Open methodology", "openSourceDesc": "Every recipe, every weight, every freshness flag is in the public registry." }, "toast": { "linkCopied": "Link copied to clipboard", "jsonCopied": "JSON copied to clipboard" }, "drawer": { "components": "Components", "subRecipes": "Sub-recipes", "weights": "Weights", "policy": "Policy", "level": "Level", "description": "Description", "minCoverage": "Min coverage", "allowsCostSignals": "Allows cost signals", "allowsArenaCalibration": "Allows arena calibration", "noComponents": "No components defined." }, "search": { "placeholder": "Search models, indexes, recipes…", "noResults": "No results", "hint": "Press / to search" }, "labs": { "title": "Labs", "subtitle": "The organizations building frontier AI models.", "compareLabs": "Compare labs" }, "compare": { "title": "Model Compare", "subtitle": "Compare models side-by-side across general intelligence indexes, raw benchmarks, and media capabilities.", "modelA": "Model A", "modelB": "Model B", "selectModel": "Select model...", "indexDelta": "Index Performance Delta", "modelAWins": "Model A +{{score}}", "modelBWins": "Model B +{{score}}", "tie": "Tie", "divergence": { "title": "Where these two models disagree the most", "body": "Top 3 indexes ranked by absolute score difference. Hover the bar chart above for raw values." }, "visualSandbox": "Visual Sandbox Comparison", "promptFidelityDesc": "Prompt fidelity and artistic rendering side-by-side.", "promptPreset": "Prompt Preset", "renderQuality": "Render Quality: {{score}}/10", "noSandboxFor": "No sandbox image preset generated for {{name}}.", "techSpecs": "Technical Specifications & Cost Profile", "resourceCategory": "Resource Category", "typeTextMultimodal": "Text / Multimodal", "typeGeneration": "{{type}} Generation", "pricingInOut": "Pricing (In / Out)", "unitCost": "Unit Cost", "throughput": "Throughput", "genLatency": "Generation Latency", "contextWindow": "Context Window", "nativeResolution": "Native Resolution", "coreFeatures": "Core Features", "noSpecs": "No specifications loaded for this model.", "benchDiffs": "Benchmark Source Differences", "table": { "variant": "Variant", "type": "Type", "rawA": "Raw A", "rawB": "Raw B", "normA": "Norm A", "normB": "Norm B", "normDiff": "Norm Diff" }, "startCta": "Start Comparison", "startDesc": "Select any two models from the dropdowns above to compare performance metrics, costs, and generative quality." }, "coverage": { "title": "Model vs Benchmark Coverage Matrix", "subtitle": "Audit benchmark density. A complete audit grid indicating which benchmark variants have been ingested and computed for each model. Shaded cells represent normalized score performance.", "pill": "Integrity & Ingestion", "exportCsv": "Export Matrix CSV", "searchPlaceholder": "Search models or labs...", "auditMatrix": "Ingestion Completeness Audit Matrix", "howToTitle": "How to interpret this audit matrix", "howToBody1": "This grid tracks raw ingest completeness. Cells displaying a numerical score represent verified normalized benchmark scores. Cells displaying a dash (—) indicate benchmark datasets that have not yet been evaluated or ingested for that model.", "howToBody2": "Coverage percentages are calculated as the number of completed benchmark variants divided by total variants. Models with higher coverage have a more stable and accurate composite index score." }, "dataQuality": { "title": "Data Quality & Ingestion Auditing", "subtitle": "Real-time status of benchmark scrapers, sync histories, quarantine metrics, and known evaluations gaps.", "pill": "Integrity, Quality & Transparency", "scraperIntegrations": "Scraper Integrations", "activeScrapers_one": "{{count}} Active Scraper", "activeScrapers_other": "{{count}} Active Scrapers", "scrapeSuccessRate": "Scrape Success Rate", "acrossLastSyncs_one": "Across last {{count}} sync attempt", "acrossLastSyncs_other": "Across last {{count}} sync attempts", "quarantinedRecords": "Quarantined Records", "awaitingAdmin": "Awaiting admin mapping", "avgModelDensity": "Average Model Density", "testedBenchmarks": "Tested benchmarks per model", "adaptersStatus": "Integration Adapters Status", "active": "Active", "inactive": "Inactive", "typeLabel": "Type: {{type}}", "scraperReliability": "Scraper Reliability", "reliabilityPct": "{{pct}}% Success", "ingestionLogs": "Scraper Ingestion Logs (Last 30 Syncs)", "colScraper": "Scraper", "colStatus": "Status", "colStartedAt": "Started At", "colDuration": "Duration", "colIngested": "Ingested", "colFailed": "Failed", "noSyncActivity": "No recent sync activity logged.", "coverageGaps": "Benchmark Coverage Gaps", "coverageGapsDesc": "Audits of models with incomplete benchmark variants records.", "searchGapsPlaceholder": "Search model gaps...", "quarantineTitle": "Quarantine & Name Aliasing", "quarantineBody1": "When scrapers pull benchmark scores for a model name that doesn’t match our canonical index listings, the system automatically redirects those scores to the Quarantine Queue.", "quarantineBody2": "Administrative review is performed daily. Once canonical aliasing maps the scraper key to the correct model profile, the score records auto-ingest, restoring downstream score coverage completeness.", "controversyLog": "Benchmark Controversy Log", "controversyLogDesc": "Statistical anomalies flagging potential model overfitting, contamination, or measurement discrepancies.", "noDiscrepancies": "No statistical discrepancies detected." }, "api": { "developerPlatform": "Developer Platform", "title": "REST API Documentation", "subtitle": "Build on top of verified model reconciliation data. Our developer platform provides public API endpoints to query models, index scores, benchmark configurations, and bulk exports in JSON or CSV.", "availableEndpoints": "Available Endpoints", "parameters": "Parameters", "required": "Required", "requestSnippets": "Request Snippets", "liveResponse": "Live API Response", "sendRequest": "Send Request", "outputPayload": "Output payload", "emptyResponse": "Click \"Send Request\" to perform a live fetch from the API and inspect the payload here.", "gs": { "title": "Quickstart", "blurb": "Five commands to get started", "body1": "The API is RESTful, returns JSON by default, and requires no auth.", "body2": "All responses are JSON unless otherwise noted. For the canonical entity model, see" }, "rl": { "title": "Rate limits", "blurb": "Generous for read endpoints", "body1": "There is no per-key rate limit on the public read endpoints. The pipeline is throttled at the source and at the CDN:", "anonymous": "Anonymous", "anonymousBody": "100 requests per minute per IP. Soft limit; bursts over 200 are returned as 429.", "cached": "Cached responses", "cachedBody": "All read endpoints are CDN-cached for 5 minutes. The same query for the same model returns the cached payload.", "bulk": "Bulk export", "bulkBody": "/export/scores and /export/results return up to 5 MB per request. Use pagination for larger pulls.", "foot": "If you need higher limits, the data is also available as a daily Parquet dump." }, "errors": { "title": "Errors", "blurb": "Standard status codes with JSON error body", "body1": "Errors are returned as JSON with an error, message, and (when relevant) details. Common cases:" }, "dataModelLink": "Data model" }, "embed": { "missingParams": "Missing Parameters", "missingParamsDesc": "Please provide \"a\" and \"b\" model slugs in query parameters, e.g. ?a=gpt-5-5&b=claude-opus-4-7" }, "timeline": { "title": "Timeline", "subtitle": "When each model was released, updated, or reached a new milestone." }, "explore": { "title": "Scatter Plot Score Explorer", "subtitle": "Correlate index performance dynamically. Select benchmark index dimensions for X and Y coordinates to visual index trade-offs. Circle size maps to overall coverage.", "pill": "Interactive Explorer", "highlightLabel": "Highlight Model / Lab", "filterPlaceholder": "Filter plot points...", "labColorIndex": "Lab Color Index", "understandingTitle": "Understanding this plot", "understanding1": "Bubble placement maps relative model scores on a 0-100 scale.", "understanding2": "Larger circles denote higher benchmark coverage (greater confidence).", "understanding3": "Hover over bubbles for full specs; click to open profile.", "tradeOffTitle": "Correlative Trade-Off Analysis: {{y}} vs {{x}}", "tradeOffSubtitle": "Scatter visualization showing {{x}} (horizontal X) against {{y}} (vertical Y).", "xScore": "{{name}} Score →", "yScore": "{{name}} Score →", "crossRefTitle": "Axes Cross-Reference Matrix ({{count}} models)" }, "ask": { "title": "AI Index Assistant", "subtitle": "Query model rankings, comparisons, and methodology with live context.", "poweredBy": "Powered by Llama 3.3", "disclaimer": "Rankings are computed using raw source data normalized dynamically. Responses represent live evaluations.", "suggestedLabel": "Suggested Questions", "placeholder": "Ask anything about model scores, pricing, or speed...", "welcomeBody": "### Welcome to the AI Model Index Explorer!\n\nI am your intelligent assistant. I have live access to the entire benchmark database.\n\nAsk me questions about:\n- **Index Rankings:** \"Who leads the Frontier Capability index?\"\n- **Model Comparisons:** \"Compare Claude 3.5 Sonnet and Gemini 3.1 Pro\"\n- **Methodology:** \"How does the robust normalization work?\"\n- **Generative Media:** \"Which video model is currently ranked #1?\"\n\nClick one of the suggested questions below to start instantly!", "suggestions": { "coding": "Which model is the best coding assistant?", "mediaIndex": "How is the Generative Media Index calculated?", "compare": "Compare Claude Opus 4.8 and GPT-5.6 Sol", "safety": "What indexes measure safety & alignment?" }, "error": { "noAnswer": "Sorry, I could not process that question.", "network": "Something went wrong. Please check your network connection and try again." } }, "seo": { "defaultTitle": "AI Model Index", "defaultDescription": "Independent, reproducible rankings, recipes, and live intelligence for every frontier AI model.", "homeTitle": "AI Model Index — Trust the recipe, not the rank.", "homeDescription": "Every ranking in the AI Model Index is the output of an explicit, versioned, public recipe. Inspect the formula. Reproduce the score. Question the inputs." }, "aria": { "openMenu": "Open menu", "closeMenu": "Close menu", "openDrawer": "Open details", "closeDrawer": "Close drawer", "loadingContent": "Loading content", "searchInput": "Search input", "languageSwitcher": "Language switcher", "navigation": "Main navigation", "skipToContent": "Skip to main content" }, "notFound": { "title": "Page Not Found", "description": "The page you are looking for does not exist or has been moved.", "searchPlaceholder": "Search models, indexes, benchmarks...", "suggestedPages": "Suggested Pages" }, "error": { "backHome": "Back to home", "suggestions": { "home": "Start from the beginning", "models": "Browse all tracked models", "indexes": "View all leaderboard indexes", "benchmarks": "Explore benchmark families", "compare": "Side-by-side model comparison", "api": "Interactive API documentation", "domainIndexes": "Specialized domain indexes", "status": "System health checks" } }, "docs": { "category": { "guide": "Guide", "reference": "Reference" }, "lastUpdatedDate": "2026-06-03", "version": "v", "lastUpdated": "Last updated", "editOnGitHub": "Edit on GitHub", "onThisPage": "On this page", "previous": "Previous", "next": "Next", "wasHelpful": "Was this page helpful?", "helpful": "Yes, helpful", "notHelpful": "Not really", "noMatches": "No matching sections.", "searchPlaceholder": "Search sections…", "searchAriaLabel": "Search within this page", "tocAriaLabel": "Table of contents", "docsNav": "Documentation", "nav": { "overview": "Overview", "glossary": "Glossary", "dataModel": "Data model", "recipes": "Recipes", "sources": "Sources", "calculations": "Calculations", "methodology": "Methodology", "api": "API reference" }, "breadcrumb": { "docs": "Docs" }, "landing": { "eyebrow": "Documentation", "title": "How the AI Model Index works", "subtitle": "Every score on this site is the output of an explicit, versioned, public recipe. These pages explain the data, the math, and the trade-offs — in enough detail to verify.", "byAudience": "Find your way in", "allPages": "All documentation pages", "audiences": { "researcher": { "title": "I'm a researcher", "blurb": "You want to understand the methodology, normalization, and confidence — and verify a number.", "items": { "mentalModel": "Mental model: how the index is built", "normalization": "How raw scores are normalized to 0–100", "confidence": "What confidence, trust, and coverage mean", "policies": "Source policy and downweighting rules" } }, "developer": { "title": "I'm a developer", "blurb": "You want to pull this data into a product or a script. The API is the source of truth.", "items": { "endpoints": "All 12 endpoints with live try-it", "tryIt": "Try it without leaving the docs", "rateLimits": "Rate limits, status codes, error format", "errors": "Common error patterns and fixes" } }, "analyst": { "title": "I'm an analyst", "blurb": "You want a concrete example: raw data in, composite out. With numbers.", "items": { "workedExample": "End-to-end worked example", "rawToIndex": "Raw score → normalized → weighted → composite", "edgeCases": "Edge cases: missing data, ties, staleness", "reproducibility": "Reproduce any score in 3 lines of curl" } }, "curious": { "title": "I'm just curious", "blurb": "You want the human-readable explanation. No math required to start.", "items": { "definitions": "Plain-language glossary of every term", "noMath": "Concepts before formulas", "quickReads": "Short, focused reads", "seeAlso": "Cross-references to deeper material" } } }, "sections": { "overview": { "title": "Overview", "blurb": "What this site is, who it is for, and the principles we follow." }, "glossary": { "title": "Glossary", "blurb": "Plain-language definitions of every term used in the index." }, "dataModel": { "title": "Data model", "blurb": "The canonical entities: models, labs, benchmarks, variants, sources." }, "recipes": { "title": "Recipes", "blurb": "How composite indexes are assembled from raw benchmarks." }, "sources": { "title": "Sources", "blurb": "Every data source we ingest, how it is rated, and how to propose a new one." }, "calculations": { "title": "Calculations", "blurb": "A worked example: raw data → composite score, step by step." }, "methodology": { "title": "Methodology", "blurb": "The 8-section deep dive on how scores are derived." }, "api": { "title": "API reference", "blurb": "All 12 endpoints with parameters, response shape, and a live tester." } }, "principles": { "title": "Our documentation principles", "intro": "Docs here are written and reviewed with the same care as the data pipeline.", "items": { "reproducible": { "title": "Reproducible", "body": "Every claim links to a source row or a recipe. You should be able to re-derive any number." }, "auditable": { "title": "Auditable", "body": "Every change is versioned and dated. The change log explains what shifted and why." }, "versioned": { "title": "Versioned", "body": "Methodology, schema, and API are versioned. v2.0.0 is meaningfully different from v1.0.0." }, "translatable": { "title": "Translatable", "body": "All documentation is available in seven locales: en, ar, zh-CN, es, pt-BR, fr, de." }, "open": { "title": "Open", "body": "Recipes, source code, and data are public. Anyone can verify, fork, or correct." }, "honest": { "title": "Honest", "body": "When we are wrong, we say so. Disputes, errors, and methodology changes are documented." } } } }, "sections": { "overview": { "title": "Overview", "blurb": "What this site is, who it is for, and the principles we follow." }, "glossary": { "title": "Glossary", "blurb": "Plain-language definitions of every term used in the index." }, "dataModel": { "title": "Data model", "blurb": "The canonical entities: models, labs, benchmarks, variants, sources." }, "recipes": { "title": "Recipes", "blurb": "How composite indexes are assembled from raw benchmarks." }, "sources": { "title": "Sources", "blurb": "Every data source we ingest, how it is rated, and how to propose a new one." }, "calculations": { "title": "Calculations", "blurb": "A worked example: raw data → composite score, step by step." }, "methodology": { "title": "Methodology", "blurb": "The 8-section deep dive on how scores are derived." }, "api": { "title": "API reference", "blurb": "All 12 endpoints with parameters, response shape, and a live tester." } }, "glossary": { "title": "Glossary", "subtitle": "Plain-language definitions of every term you will see on the site, sorted by category.", "intro": "If a term is unclear in another doc, the canonical definition lives here.", "searchPlaceholder": "Search terms…", "noResults": "No terms match \"{{query}}\".", "seeAlso": "See also", "filter": { "all": "All", "core": "Core concepts", "metric": "Metrics", "pipeline": "Pipeline", "data": "Data" }, "category": { "core": "Core", "metric": "Metric", "pipeline": "Pipeline", "data": "Data" }, "terms": { "index": { "term": "Index", "definition": "A composite ranking of models built from one or more weighted components. The Production Index, the Coding Index, and the Generative Media Index are all indexes. Each index has a recipe that determines its components and weights.", "examples": "Production Index, Coding Index, Generative Media Index", "seeAlso": "Recipes, Components" }, "recipe": { "term": "Recipe", "definition": "A versioned, public description of how an index is computed. A recipe names the components, their weights, the normalization policy, and the freshness window. Every score in the index is reproducible from its recipe.", "examples": "production-v2, coding-v1, media-v1", "seeAlso": "Index, Component, Weight" }, "component": { "term": "Component", "definition": "A named bucket within an index that groups one or more variants by theme. The Production Index has Capability, Coding, Math, Speed, and Cost components. Components have weights that sum to 1.0.", "seeAlso": "Index, Weight, Variant" }, "variant": { "term": "Variant", "definition": "A specific evaluation within a benchmark family. \"SWE-bench Verified\" and \"SWE-bench Lite\" are variants of the same family. Variants have a direction (higher/lower is better), a unit, and a trust score.", "examples": "swe-bench-verified, swe-bench-lite, livecodebench-q3-2025", "seeAlso": "Benchmark, Source, Trust" }, "source": { "term": "Source", "definition": "An external system that publishes benchmark results. Each source has a trust score derived from methodology independence, reproducibility, coverage, transparency, and recency.", "seeAlso": "Trust, Source family" }, "sourceFamily": { "term": "Source family", "definition": "A grouped set of sources that share methodology. Artificial Analysis (provider data) and LMArena (crowd arena) are different source families. A source family has a signal type: pure_model, agent_model, or provider.", "seeAlso": "Source, Trust, Signal type" }, "model": { "term": "Model", "definition": "A specific AI model identifier. Models have a canonical slug, a list of aliases (e.g. dated versions), a lab, a release date, and modalities. A model is the unit of ranking.", "examples": "gpt-4o, claude-3.5-sonnet, llama-3.1-405b", "seeAlso": "Alias, Lab" }, "lab": { "term": "Lab", "definition": "The organization that produces a model. OpenAI, Anthropic, and Meta are labs. Labs are credited in acknowledgments and serve as the natural aggregation key for lab-level indexes.", "seeAlso": "Model, Source" }, "alias": { "term": "Alias", "definition": "An alternative identifier for the same canonical model. \"gpt-4o-2024-05-13\" and \"gpt-4o-2024-08-06\" are aliases of the canonical \"gpt-4o\". Aliases are normalized at ingestion time.", "seeAlso": "Model" }, "normalization": { "term": "Normalization", "definition": "The process of mapping a raw result (Elo, %, tok/s, USD) onto a 0–100 scale using robust percentile-based mapping. After normalization, scores across different variants are directly comparable.", "seeAlso": "Variant, Coverage, Confidence" }, "coverage": { "term": "Coverage", "definition": "The fraction of an index's intended weight that has data for a given model. A model with 100% coverage has data for every component. A model with 40% coverage is partially scored and should be interpreted with caution.", "seeAlso": "Component, Weight" }, "confidence": { "term": "Confidence", "definition": "A combined score reflecting source trust, per-result confidence, and coverage. High confidence means the score is well-supported by available data. Low confidence means the data is thin, stale, noisy, or from a less-trusted source.", "seeAlso": "Trust, Coverage, Source" }, "trust": { "term": "Trust", "definition": "A 0–1 score per source reflecting methodology independence, reproducibility, coverage, transparency, and recency. Trust propagates into the per-component confidence.", "seeAlso": "Source, Confidence" }, "freshness": { "term": "Freshness", "definition": "An exponential decay factor based on the age of the most recent result for a given (model, variant) pair. Older results contribute less weight, so the index tends to track the frontier.", "seeAlso": "Variant, Normalization" }, "weight": { "term": "Weight", "definition": "A 0–1 number expressing the relative importance of a component within an index. Weights within an index sum to 1.0. Weights can be revised when a new recipe version is published.", "seeAlso": "Component, Recipe" }, "composite": { "term": "Composite score", "definition": "The final number for a model on an index. It is the sum of (component weight × normalized score) over all components with data, divided by coverage.", "seeAlso": "Normalization, Component, Coverage" }, "policy": { "term": "Policy", "definition": "A rule that controls how a source's data is treated in the index. The five policies are: weighted, downweighted, provisional, display_only, and blocked. See the Methodology page for definitions.", "seeAlso": "Source, Methodology" }, "ingestion": { "term": "Ingestion", "definition": "The pipeline that fetches, normalizes, and stores benchmark results from a source. Each source has its own ingest function in netlify/functions/ingest-*.", "seeAlso": "Source, Workflow" }, "recompute": { "term": "Recompute", "definition": "The daily workflow that re-derives every composite index score from the latest raw data. Recompute runs are tracked in the change log.", "seeAlso": "Workflow, Index, Snapshot" }, "workflow": { "term": "Workflow", "definition": "A scheduled or on-demand job in the platform. The main workflows are: daily-update, recompute-indexes, and intelligence-analysis. Every workflow run is recorded with a status, timestamp, and output.", "seeAlso": "Recompute, Ingestion" }, "snapshot": { "term": "Snapshot", "definition": "A point-in-time capture of all (model, index) composite scores, used for the change log and for the per-model history endpoint.", "seeAlso": "Recompute, Workflow" }, "quarantine": { "term": "Quarantine", "definition": "A holding area for benchmark results that fail validation. Quarantined rows are not surfaced in the public index until a human reviews and approves them.", "seeAlso": "Workflow, Ingestion" } } }, "dataModel": { "title": "Data model", "subtitle": "The canonical entities that make up the index, and the relationships between them.", "fieldsKey": "Key fields", "fieldsUnique": "is the unique identifier used across the site and API", "fieldsAliases": "are alternative identifiers (dated versions, nicknames) that resolve to the same model", "fieldsLab": "points at the lab entity", "benchmarkFamily": "the umbrella group this benchmark belongs to", "benchmarkUnit": "the unit of measurement (%, Elo, tok/s, USD)", "benchmarkTrust": "the source trust score that flows into confidence", "sourceDeepLink": "See the full source reference", "calculationsLink": "See Calculations for a worked example", "lifecycleEnd": "explains how these entities evolve over time", "table": { "relationship": "Relationship", "from": "From", "to": "To", "cardinality": "Cardinality" }, "sections": { "overview": { "title": "Overview", "body1": "The AI Model Index is a relational system built around a small set of canonical entities. Every page on the site, every API endpoint, and every recipe resolves to one of these entities and the relationships between them.", "body2": "This page documents the schema in plain language. For the currently registered read-only routes, see /docs/api." }, "entities": { "title": "Entities", "body1": "There are seven first-class entities. Six are data (model, lab, benchmark family, variant, source, score row) and one is structural (index, which composes variants into a ranked list)." }, "model": { "title": "Model", "body1": "A model is the unit of ranking. It has a canonical slug, a lab, a release date, modalities, a context window, and pricing where available. It also has a list of aliases." }, "benchmark": { "title": "Benchmark family", "body1": "A benchmark family groups related evaluations. SWE-bench is a family; SWE-bench Verified and SWE-bench Lite are variants within that family. A family is described by:" }, "variant": { "title": "Variant", "body1": "A variant is one specific evaluation. It has a direction (higher or lower is better), a unit, a source, and a trust score. Variants are the leaf nodes that feed into indexes.", "body2": "Variants also have a freshness policy and a normalization strategy (robust percentile, win-rate, z-score). These are declared in the recipe." }, "source": { "title": "Source", "body1": "A source is where a variant's data comes from. Each source has a trust score and a license. Sources are credited on the Acknowledgments page.", "body2": "For a full source reference, including license, freshness, and pipeline details," }, "relationships": { "title": "Relationships", "body1": "The cardinalities below describe the actual implementation in Postgres. Use this as the reference when querying the database or designing a downstream system." }, "lifecycle": { "title": "Lifecycle", "body1": "Entities move through a five-stage pipeline. Each stage has a corresponding table and a corresponding admin page:", "ingest": "Ingestion", "ingestBody": "raw rows are fetched from a source and stored in benchmark_results", "reconcile": "Reconciliation", "reconcileBody": "duplicate and conflicting rows are resolved; aliases are merged", "normalize": "Normalization", "normalizeBody": "raw values are mapped onto a 0–100 scale per variant", "composite": "Composition", "compositeBody": "normalized values are weighted and summed into a composite per (model, index)", "snapshot": "Snapshot", "snapshotBody": "the current state is frozen as a snapshot for the change log" } } }, "recipes": { "title": "Recipes", "subtitle": "How a composite index is assembled from raw benchmark signals — components, weights, and the versioned schema that controls them.", "callout": { "title": "Recipes are public", "body": "Every recipe is a JSON object checked into the repository. Anyone can audit, fork, or propose a change. The explorer at /recipes makes them browsable." }, "explorerLink": "Recipe Explorer", "anatomy": { "id": "stable identifier, e.g. \"production-v2\"", "name": "human-readable name, e.g. \"Production Index v2\"", "description": "one-paragraph explanation of what the recipe measures", "components": "ordered list of components with their weights and variants", "parameters": "named parameters like lookback_days, min_results, outlier_threshold", "policies": "per-source policy overrides (weighted, downweighted, etc.)", "version": "semver of the recipe itself" }, "weightExample": { "title": "Weighted aggregation example" }, "families": { "frontier": "Frontier Capability", "frontierBody": "broad capability across reasoning, knowledge, math, and code", "coding": "Coding & App-Building", "codingBody": "professional coding, SWE tasks, terminal use, code arena", "media": "Generative Media", "mediaBody": "image and video generation, design quality, prompt following", "value": "Production Value", "valueBody": "production-ready models weighted by cost, speed, and reliability", "agent": "Agent & Tool Use", "agentBody": "agent-style benchmarks (downweighted in pure-model indexes)" }, "governance": { "propose": "Propose", "proposeBody": "open an issue with the proposed change and rationale", "review": "Review", "reviewBody": "maintainers and the community discuss; impact is shown in a dry-run", "diff": "Diff", "diffBody": "the change is rendered as a structured diff against the current recipe", "publish": "Publish", "publishBody": "the new version becomes active; old scores are recomputed retroactively", "archive": "Archive", "archiveBody": "the prior version is preserved and continues to be queryable" }, "changelogLink": "Changelog", "examples": { "frontier": "Frontier Capability", "frontierBody": "AA Intelligence (60%) + LMArena Text (40%), downweighted for stale results", "value": "Production Value", "valueBody": "Production (50%) + Speed (20%) + Cost (30%), all in 0–100" }, "calculationsLink": "Calculations page", "examplesEnd": "for a worked example that turns these into a number", "sections": { "what": { "title": "What is a recipe?", "body1": "A recipe is a versioned, public description of how a composite index is computed. It is the single source of truth for what the index measures, how it is weighted, and how it ages.", "body2": "If a number on the site cannot be reproduced from a recipe, the number is wrong. This is the design contract.", "body3": "You can browse all recipes visually in the", "body4": "page" }, "anatomy": { "title": "Anatomy of a recipe", "body1": "Every recipe has the same shape. The fields are:" }, "parameters": { "title": "Parameters", "body1": "Parameters control how the recipe behaves. The most common are:", "body2": "Each parameter is documented inline. Default values are chosen so a recipe is meaningful without any tuning." }, "weights": { "title": "Weights", "body1": "Weights express the relative importance of each variant within a component. Weights are non-negative and normalized so each component sums to 1.0.", "body2": "The example on the right shows a typical weighted aggregation: 40% HumanEval, 30% SWE-bench, 20% Vibe Code, 10% Code Arena." }, "composition": { "title": "Composition", "body1": "Composition is the assembly of an index from components. Each component has a weight; each variant within a component has a weight; the normalized scores are multiplied through and summed.", "body2": "Here is a small example of a real recipe (Production Index):" }, "families": { "title": "Recipe families", "body1": "Recipes cluster into families that share a measurement intent:" }, "governance": { "title": "Recipe governance", "body1": "Recipes are versioned with semver. The lifecycle is:", "body2": "Every published change is recorded in the" }, "examples": { "title": "Examples", "body1": "Two representative recipes:" } } }, "sources": { "title": "Sources", "subtitle": "Every data source we ingest, how each is rated, and the pipeline that turns raw feeds into a row in the database.", "sourcesLink": "/sources", "acknowledgmentsLink": "Acknowledgments", "issueLink": "Open a source request on GitHub", "sections": { "overview": { "title": "Overview", "body1": "The AI Model Index is a meta-index: it does not run its own benchmarks. It ingests results from external sources and reconciles them into a single comparable space.", "body2": "Every source we trust is listed on", "body3": "and credited in" }, "families": { "title": "Source families", "body1": "Sources cluster into families by methodology. Different families contribute different signal types and are subject to different policies." }, "trust": { "title": "Source trust", "body1": "Each source has a trust score from 0 to 1, derived from five factors:", "body2": "Trust is reviewed quarterly. Changes to a source's trust are announced in the change log." }, "freshness": { "title": "Freshness", "body1": "Freshness measures how recent a source's data is. Each variant has a half-life: results older than the half-life contribute half as much as fresh results.", "body2": "The decay is exponential, so a single very old result does not drag the index, but a steady stream of stale data does.", "formula": "Freshness formula", "formulaBody": "freshness = exp(−age_days / half_life_days). A 30-day half-life means a 60-day-old result contributes 25% of the weight of a fresh one." }, "license": { "title": "License and attribution", "body1": "Every source is ingested under its published terms. We honor three categories:", "body2": "If you are a source maintainer and you would like to update how your data is presented, please open an issue." }, "pipeline": { "title": "Ingestion pipeline", "body1": "Each source has a dedicated ingest function in netlify/functions/ingest-*. The pipeline is:", "fetch": "Fetch", "fetchBody": "pull raw data from the source (API, CSV, JSON, scrape)", "normalize": "Normalize", "normalizeBody": "map source-specific shapes into a canonical row format", "alias": "Alias", "aliasBody": "resolve model aliases to the canonical slug", "quarantine": "Quarantine", "quarantineBody": "rows that fail validation are routed to a quarantine table", "approve": "Approve", "approveBody": "an admin reviews quarantined rows and either approves or rejects" }, "adding": { "title": "Adding a new source", "body1": "We add new sources when they are methodologically independent, publicly accessible, and cover a meaningful slice of the model space.", "body2": "To propose a new source," } }, "families": { "arena": { "title": "Arena", "body": "Crowdsourced human preference (LMArena, EQ-Bench arena). High coverage, lower reproducibility." }, "labOfficial": { "title": "Lab official", "body": "Results published by the lab itself (OpenAI evals, Anthropic evals). High trust for that lab, lower for cross-lab comparison." }, "crowd": { "title": "Crowd benchmark", "body": "Community-maintained benchmarks (HumanEval+, BigCodeBench). Moderate trust, fast coverage of new models." }, "crowdPro": { "title": "Pro benchmark", "body": "Professionally maintained benchmarks (SWE-bench Verified, Vals). High trust, slower coverage of new models." }, "openDataset": { "title": "Open dataset", "body": "HuggingFace leaderboard datasets. Trust depends on the dataset maintainer; trust is reviewed per dataset." } }, "trust": { "factors": { "methodology": "Methodology", "methodologyBody": "Is the methodology documented? Is the test set public? Is contamination controlled?", "reproducibility": "Reproducibility", "reproducibilityBody": "Can the result be reproduced from a public model? Is the seed fixed?", "coverage": "Coverage", "coverageBody": "How many models does the source cover? How quickly does it pick up new models?", "transparency": "Transparency", "transparencyBody": "Are the underlying scores, raw outputs, and sample sizes public?", "recency": "Recency", "recencyBody": "How often is the source updated? Are stale results retired?" } }, "license": { "derived": "Derived data", "derivedBody": "We compute our own scores from the source. We publish the source and the recipe.", "attribution": "Attribution required", "attributionBody": "The source is shown on every result row and on the Acknowledgments page.", "canonical": "Canonical reference", "canonicalBody": "We link to the source's own page whenever a score is displayed." }, "pipeline": { "fetch": "Fetch", "fetchBody": "pull raw data from the source (API, CSV, JSON, scrape)", "normalize": "Normalize", "normalizeBody": "map source-specific shapes into a canonical row format", "alias": "Alias", "aliasBody": "resolve model aliases to the canonical slug", "quarantine": "Quarantine", "quarantineBody": "rows that fail validation are routed to a quarantine table", "approve": "Approve", "approveBody": "an admin reviews quarantined rows and either approves or rejects" }, "adding": { "issueLink": "Open a source request on GitHub" } }, "calc": { "title": "Calculations", "subtitle": "A worked example. Raw data in, composite out, every step shown.", "intro": { "body1": "This page walks one model —", "model": "gpt-4o", "vs": "— through the full pipeline to its final score on the", "index": "Production Index" }, "apiLink": "API reference", "sections": { "overview": { "title": "Overview", "body1": "The goal is transparency: given a model and an index, you should be able to reproduce its score from public data and a public recipe.", "body2": "We use a real model (gpt-4o) and a real recipe (Production Index v2). All numbers in this page come from the actual database." }, "raw": { "title": "Step 1 — Raw inputs", "body1": "Each variant publishes results in its own units. The first step is to collect them as-is, before any normalization." }, "normalize": { "title": "Step 2 — Normalize to 0–100", "body1": "Each variant's raw results are mapped to a 0–100 scale using a robust percentile-based formula. This makes different units directly comparable.", "body2": "For higher-is-better variants, 0 is the 10th percentile and 100 is the 90th percentile. For lower-is-better variants (like price), the direction is inverted before the same mapping is applied.", "body3": "The formula combines a linear mapping (70% weight) with the global percentile rank (30% weight) for robustness against outliers:", "body4": "Applying this to the six variants gives:" }, "weight": { "title": "Step 3 — Apply recipe weights", "body1": "The Production Index v2 recipe has five components with the following weights and variants:" }, "composite": { "title": "Step 4 — Composite score", "body1": "The composite score is the weighted sum of the normalized variant scores:", "body2": "So gpt-4o's score on the Production Index v2 is 68.28 at this snapshot." }, "coverage": { "title": "Step 5 — Coverage", "body1": "Coverage is the fraction of the recipe's intended weight that has data for this model:", "body2": "In this case, all six variants have data, so coverage is 100%. The composite is reported without further adjustment. If coverage were 50%, the composite would be flagged and the displayed number would be shown with a coverage badge." }, "confidence": { "title": "Step 6 — Confidence", "body1": "Confidence is a product of three factors:" }, "edge": { "title": "Edge cases", "body1": "The pipeline handles four common edge cases explicitly:", "missingBody": "if a variant has no row, its weight is excluded from the numerator and the denominator; coverage drops accordingly", "tiesBody": "ties at a percentile boundary are broken by the median of the affected values", "outliersBody": "values beyond p10 / p90 are clipped to the boundary, so a single extreme result cannot dominate the index", "staleBody": "results older than the variant's half-life are down-weighted exponentially" }, "verify": { "title": "Verify it yourself", "body1": "Every step of this example is reproducible from the API.", "body2": "Pull the live score for the same model on the same index from the" } }, "table": { "variant": "Variant", "unit": "Unit", "direction": "Direction", "raw": "Raw", "p10": "p10", "p90": "p90", "normalized": "Normalized", "component": "Component", "weight": "Weight", "componentWeight": "Effective weight", "total": "Total" }, "normalize": { "formula": "Normalization formula" }, "confidence": { "trust": { "title": "Source trust", "body": "How much we trust the underlying source (0–1)." }, "perResult": { "title": "Per-result confidence", "body": "How much we trust the specific row, based on sample size, recency, and noise." }, "coverage": { "title": "Coverage", "body": "How much of the recipe's weight is present for this model." } }, "edge": { "missing": "Missing data", "ties": "Ties at percentile boundary", "outliers": "Outliers", "stale": "Stale results" } } }, "articleReader": { "notFoundTitle": "Article Not Found", "notFoundDescription": "The requested article could not be found.", "notFoundHeading": "Article not found", "notFoundBody": "The article you are looking for does not exist.", "published": "Published", "updated": "Updated", "sharePrompt": "Did you find this useful? Share it with your team.", "share": "Share", "related": "Related Articles", "machineTranslated": "This article has been machine-translated. Translations are cached and may lag behind the original." }, "methodologyArticlePage": { "notFound": "Not Found", "bodyMissing": "Article not found.", "deepDive": "Deep Dive", "minRead_one": "{{count}} min read", "minRead_other": "{{count}} min read", "contentUnavailable": "Content unavailable." }, "changelog": { "title": "Changelog", "subtitle": "Track model additions, score updates, and data pipeline runs.", "recentRuns": "Recent Pipeline Runs", "noRuns": "No runs found.", "status": "Status", "scoresComputed_one": "Scores: {{count}}", "scoresComputed_other": "Scores: {{count}}", "newModels_one": "New Model ({{count}})", "newModels_other": "New Models ({{count}})", "moreCount_one": "+{{count}} more", "moreCount_other": "+{{count}} more", "scoreChanges_one": "Score Change ({{count}})", "scoreChanges_other": "Score Changes ({{count}})", "table": { "model": "Model", "index": "Index", "previous": "Previous", "current": "Current", "delta": "Δ" }, "empty": "No score changes detected yet. Run the daily update pipeline to generate history.", "rss": "Subscribe via RSS", "rssTitle": "Subscribe to a changelog RSS feed", "filterByIndex": "Filter by index", "filterAll": "All indexes" }, "familySpotlight": { "unknownFamily": "Unknown source family: {{slug}}", "unknownDesc": "We could not find a benchmark family with that slug. Try the full source list.", "allSources": "All Sources", "editorialRole": "Editorial role", "statVariants": "Variants", "statTrust": "Trust", "statPolicies": "Policies", "statSignals": "Signal types", "variantsHeading_one": "Variant ({{count}})", "variantsHeading_other": "Variants ({{count}})", "table": { "variant": "Variant", "type": "Type", "direction": "Direction", "signal": "Signal", "policy": "Policy", "trust": "Trust", "freshness": "Freshness" }, "higherBetter": "↑ higher is better", "lowerBetter": "↓ lower is better", "topModelsHeading": "Top models using this family", "topModelsDesc": "Average normalized score across the {{count}} variants in this family, weighted by component weight. Only models with at least 20% variant coverage are shown.", "coverageLabel": "Coverage", "acrossVariants_one": "across {{count}} variant", "acrossVariants_other": "across {{count}} variants", "ingestHeading": "What gets ingested from this family" }, "acknowledgments": { "title": "Acknowledgments", "intro": "The AI Model Index is built on the work of benchmark creators, open-source contributors, and research teams around the world. This page is our attempt to give credit where it is due.", "dataSources": "Data Sources", "dataSourcesDesc": "Every source below is linked directly. We do not own this data — we normalize, reconcile, and composite it. Please visit the original sources for raw rankings and methodology details.", "visitSource": "Visit source", "people": "People & Teams", "peopleDesc": "These researchers, engineers, and teams built the benchmarks and infrastructure that make this index possible. We are grateful for their transparency and rigor.", "creatorCtaLead": "Are you a benchmark creator?", "creatorCtaBody": "If you maintain a source included here and would like your name, affiliation, or social profile linked, please reach out. We want this page to be as complete and accurate as possible.", "openSource": "Open Source", "openSourceDesc": "This project is built entirely on open-source software. Thank you to every contributor who maintains these tools.", "contributeTitle": "Want to contribute?", "contributeBody": "The AI Model Index is an independent, community-driven project. If you want to suggest a source, report a bug, or help improve the methodology, we would love to hear from you.", "readMethodology": "Read Methodology", "browseSources": "Browse Sources", "license": "License", "lastReviewed": "Last reviewed", "contributeGithub": "Propose a source on GitHub" }, "lab": { "notFound": "Lab not found.", "modelsIndexed": "{{count}} model indexed", "modelsIndexed_other": "{{count}} models indexed", "trajectory": "Lab Performance Trajectory", "benchmarkResults": "Benchmark Results", "topIndexRankings": "Top Index Rankings", "colModel": "Model", "colBenchmark": "Benchmark", "colFamily": "Family", "colRawValue": "Raw Value", "colNormalized": "Normalized", "colConfidence": "Confidence", "rank": "rank {{rank}}", "rankHash": "Rank #{{rank}}" }, "correlations": { "title": "Index Correlations", "subtitle": "Analyze Pearson correlation coefficients (r) between capability indexes. Detect synergies and alignment trade-offs.", "exportCsv": "Export Matrix CSV", "inspector": "Correlation Inspector", "indexA": "Index A", "indexB": "Index B", "pearsonLabel": "Pearson Correlation (r)", "basedOn_one": "Based on {{count}} model with scores in both indexes", "basedOn_other": "Based on {{count}} models with scores in both indexes", "interpretation": "Interpretation", "explainer": "Pearson's correlation coefficient (r) ranges from -1.0 (perfect negative tradeoff) to +1.0 (perfect positive synergy). A value of 0 indicates no linear relationship.", "selectCell": "Select a Cell", "selectCellBody": "Click on any cell in the correlation matrix grid to view detailed interpretation and metrics." }, "genMedia": { "specialty": "Specialty Showcase", "title": "Generative Media Leaderboard", "subtitle": "Discover the leading systems in creative AI. We reconcile image, video, and audio benchmarks into a unified index. Drill down by category to compare capabilities, speeds, and generation costs.", "lab": "Lab", "specs": "Specs", "scoreBreakdown": "Score Breakdown", "noSpecs": "No specifications", "playground": "Image Quality Comparison Playground", "rankingsFor": "{{tab}} Rankings", "modelsRegistered_one": "{{count}} Model registered", "modelsRegistered_other": "{{count}} Models registered", "backedBy": "Backed by Artificial Analysis arena Elo scores · LMArena human-preference calibration · Design Arena visual Elo", "featured": "Featured generative models", "featuredDesc": "Top performers from the current Artificial Analysis leaderboards across image, video, and audio.", "capabilitiesTitle": "Capabilities that matter", "photorealism": "Photorealism", "photorealismDesc": "Skin pores, micro-detail, and specular response to lighting — measured against AA arena Elo reference photos.", "textRendering": "Text rendering", "textRenderingDesc": "Readable in-image typography — the hardest multimodal problem. Evaluated via AA and LMArena human preference.", "temporalCoherence": "Temporal coherence", "temporalCoherenceDesc": "Object permanence and consistent characters across a 10s clip. AA Video Quality is the backbone signal.", "voiceCloning": "Voice cloning", "voiceCloningDesc": "Speaker similarity from a short reference. AA Speech Arena Elo measures naturalness and fidelity.", "costEfficiency": "Cost efficiency", "costEfficiencyDesc": "Quality-per-dollar. Cost is displayed as context only — media indexes are scored purely on quality signals.", "throughput": "Throughput" }, "labsCompare": { "pill": "B2B Industry Analytics", "title": "Lab Head-to-Head Comparison", "subtitle": "Audit leading AI providers across multiple dimensions. Capability index ratings represent top 3 models average scores.", "selectLabs": "Select Labs to Compare:", "radarTitle": "Lab Capability Radar Matrix", "trajectoryTitle": "Index Trajectory progression comparison", "matrixTitle": "Current Index Averages Matrix" }, "doctor.title": "Find the right model for your use case", "doctor.subtitle": "Answer four questions. Get a tailored shortlist from 180+ models, ranked by your needs — not by sponsor.", "roadmap.title": "What we're building next", "roadmap.subtitle": "A living document of shipped, in-flight, and planned features. Updated every release. No vapor, no stealth.", "press.title": "Press kit, fact sheet, and media coverage", "press.subtitle": "AI Model Index is a free, open-data leaderboard for AI models. Use the resources below for accurate reporting.", "theme": { "light": "Light", "dark": "Dark", "toggle": "Toggle theme", "description": "Choose your preferred appearance. This preference is stored locally in your browser." }, "scoreBreakdown": { "compositeScore": "Composite Score", "index": "Index", "rank": "Rank", "confidence": "Confidence", "coverage": "Coverage", "coverageDensity": "Coverage Density", "missing": "Missing", "provisional": "Provisional", "downweighted": "Downweighted", "componentScores": "Component Scores", "rawContributions": "Raw Contributions Log", "component": "Component", "benchmark": "Benchmark", "source": "Source", "rawValue": "Raw Value", "normalized": "Norm.", "policy": "Policy", "effectiveWeight": "Eff. Wt", "points": "Points", "coveragePercent": "Coverage {{percent}}%", "reproducibilityManifest": "Reproducibility Manifest", "downloadJson": "Download JSON Manifest", "downloadCsv": "Download CSV", "reproduce": "Reproduce Score", "scoreBreakdown": "Score Breakdown" }, "indexTable": { "index": "Index", "trend": "Trend", "noScores": "No scores available.", "exportCsv": "Export CSV" }, "articleEvidence": { "title": "Primary evidence", "intro": "These captures are editorial snapshots of the linked source pages. Each card separates what the source can establish from what still needs independent testing.", "boundary": "Evidence boundary", "openSource": "Open source", "checked": "checked", "captureNote": "Visual captures are editorial snapshots. The source-level checked date records our latest review; always follow the live source for subsequent changes." } }