{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"open-llm-leaderboard","slug":"open-llm-leaderboard","name":"Open LLM Leaderboard","type":"benchmark","url":"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard","page_url":"https://unfragile.ai/open-llm-leaderboard","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"open-llm-leaderboard__cap_0","uri":"capability://data.processing.analysis.standardized.benchmark.evaluation.pipeline","name":"standardized-benchmark-evaluation-pipeline","description":"Automatically evaluates open-source LLMs against a fixed suite of standardized benchmarks (MMLU, HellaSwag, ARC, TruthfulQA, GSM8K, MATH, Winogrande) using a containerized evaluation harness. The pipeline normalizes model inputs, handles tokenization differences across architectures, and produces comparable scores across thousands of models by running identical prompts and evaluation logic against each model's inference endpoint.","intents":["Compare performance of different open-source models on standardized tasks without running evaluations locally","Understand how a specific model ranks against peers on multiple reasoning and knowledge benchmarks","Track model performance improvements over time as new versions are released","Identify which models excel at specific task categories (math, common sense, factuality)"],"best_for":["ML researchers evaluating model selection for production deployments","Open-source model developers benchmarking their releases","Teams comparing open-source alternatives to closed-source APIs","Organizations building model selection criteria for fine-tuning or deployment"],"limitations":["Benchmarks are static snapshots — don't capture real-world performance on domain-specific tasks","Evaluation methodology may not reflect how models perform with different prompting strategies or system prompts","Models must be hosted on Hugging Face Model Hub or accessible via API — private/local models cannot be evaluated","Benchmark suite is English-only; multilingual performance not captured","Evaluation latency means leaderboard updates lag behind model releases by hours to days"],"requires":["Model must be published to Hugging Face Model Hub or have public API endpoint","Model must support text generation (no vision-only or embedding-only models)","Internet connectivity to access evaluation infrastructure","No local setup required — evaluation runs on Hugging Face infrastructure"],"input_types":["model identifiers (Hugging Face model card paths)","benchmark prompt datasets (MMLU, HellaSwag, etc.)","model inference parameters (temperature, max_tokens)"],"output_types":["structured benchmark scores (numeric percentages per benchmark)","ranked leaderboard table with model metadata","performance aggregation (average score across benchmarks)","historical performance trends"],"categories":["data-processing-analysis","testing-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_1","uri":"capability://data.processing.analysis.multi.benchmark.aggregation.and.ranking","name":"multi-benchmark-aggregation-and-ranking","description":"Combines results from 7+ independent benchmarks into a unified leaderboard ranking using weighted aggregation logic. The system normalizes scores across benchmarks with different scales (0-100 vs 0-1), handles missing evaluations gracefully, and produces both overall rankings and per-benchmark breakdowns. Ranking algorithm weights benchmarks to reflect different capability dimensions (knowledge, reasoning, common sense, math).","intents":["Get a single overall score to quickly identify the best-performing models without analyzing individual benchmarks","Understand which models are strongest in specific capability areas (math vs. common sense vs. factuality)","Compare models fairly when some have incomplete evaluation results","Identify trade-offs between models (e.g., strong on MMLU but weak on math)"],"best_for":["Decision-makers selecting a single model for deployment who need a quick ranking","Model developers understanding their model's strengths and weaknesses across dimensions","Teams building model selection logic that needs to weight different benchmark types"],"limitations":["Aggregation weights are fixed by Hugging Face — no customization for domain-specific priorities","Missing benchmark results for some models can skew rankings if aggregation doesn't handle sparse data well","Equally weights all benchmarks regardless of their relevance to specific use cases (e.g., code generation not benchmarked)","Does not account for inference cost, latency, or model size — only accuracy metrics"],"requires":["Models must have completed at least some subset of benchmarks to appear in rankings","Benchmark evaluation infrastructure (same as standardized-benchmark-evaluation-pipeline)"],"input_types":["individual benchmark scores (numeric results from MMLU, HellaSwag, etc.)","model metadata (parameter count, architecture type)"],"output_types":["overall composite score (single number 0-100)","per-benchmark breakdown (scores for each benchmark)","ranked leaderboard position","capability dimension scores (e.g., 'reasoning', 'knowledge')"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_2","uri":"capability://automation.workflow.real.time.leaderboard.updates.with.model.submission","name":"real-time-leaderboard-updates-with-model-submission","description":"Provides a submission mechanism where model developers can register new models for automatic evaluation, triggering the evaluation pipeline asynchronously. The system queues submissions, runs evaluations in the background, and updates the leaderboard in real-time as results complete. Integrates with Hugging Face Model Hub API to automatically detect new model versions and re-evaluate them.","intents":["Submit a newly trained model for evaluation without manual benchmark setup","Track when a model's evaluation results are ready and see it appear on the leaderboard","Automatically re-evaluate a model when a new version is pushed to Hugging Face","Monitor evaluation progress and queue status for submitted models"],"best_for":["Model developers releasing new versions and wanting immediate benchmark feedback","Research teams publishing models and needing quick validation against baselines","Organizations running continuous model training pipelines that need automated evaluation"],"limitations":["Evaluation queue can have significant latency during high-submission periods (hours to days)","No priority/expedited evaluation option — all submissions treated equally","Requires model to be public on Hugging Face; private models cannot be evaluated","No feedback loop for failed evaluations — developers must manually debug if a model fails to evaluate","Evaluation happens once per model version — no ability to re-run with different parameters"],"requires":["Hugging Face account with model publishing permissions","Model must be in a supported format (transformers, safetensors, etc.)","Model card with proper metadata (architecture, training data, license)"],"input_types":["Hugging Face model identifier (org/model-name)","model card metadata (optional submission parameters)"],"output_types":["submission confirmation with queue position","evaluation status updates (queued, running, completed)","benchmark results once evaluation finishes","leaderboard entry with ranking"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_3","uri":"capability://search.retrieval.interactive.leaderboard.filtering.and.search","name":"interactive-leaderboard-filtering-and-search","description":"Provides a web UI with dynamic filtering and search capabilities to explore the leaderboard across multiple dimensions: model size (parameters), architecture type (Llama, Mistral, etc.), license type, and benchmark scores. Uses client-side filtering with server-side data to enable real-time exploration without page reloads. Supports sorting by any benchmark or composite score.","intents":["Find the best model within a specific parameter budget (e.g., best 7B model)","Compare models of the same architecture to understand performance scaling","Identify open-source models with permissive licenses suitable for commercial use","Explore trade-offs between model size and performance on specific benchmarks"],"best_for":["Engineers selecting models for resource-constrained deployments","Teams evaluating licensing constraints for commercial products","Researchers studying scaling laws and model architecture trade-offs","Non-technical stakeholders exploring model options via UI"],"limitations":["Filtering is limited to pre-defined dimensions — cannot filter by custom criteria (e.g., 'models trained on code')","Search is basic keyword matching on model names — no semantic search or description matching","Leaderboard data is static snapshots — filtering happens on cached data that may be hours old","No ability to save custom views or create comparison baskets","Mobile UI may be limited for exploring large leaderboards with many columns"],"requires":["Web browser with JavaScript enabled","Internet connectivity to Hugging Face Spaces infrastructure","No authentication required — leaderboard is publicly accessible"],"input_types":["filter selections (model size range, architecture, license)","sort column selection (benchmark name or composite score)","search query (model name or organization)"],"output_types":["filtered leaderboard table","model cards with detailed metadata","benchmark score details for selected models","downloadable data (CSV export of filtered results)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_4","uri":"capability://memory.knowledge.benchmark.methodology.transparency.and.documentation","name":"benchmark-methodology-transparency-and-documentation","description":"Publishes detailed documentation of evaluation methodology including: exact prompts used for each benchmark, evaluation code (open-source), model inference parameters, and rationale for benchmark selection. Maintains a GitHub repository with evaluation scripts, allowing external auditing and reproduction of results. Includes versioning of evaluation methodology to track changes over time.","intents":["Understand exactly how models are being evaluated to assess fairness and relevance","Reproduce benchmark results locally to verify leaderboard scores","Identify potential biases or issues in evaluation methodology","Adapt evaluation methodology for domain-specific benchmarking"],"best_for":["Researchers auditing leaderboard methodology for research papers","Organizations building internal benchmarks based on open-source methodology","Model developers debugging why their model underperformed on specific benchmarks","Teams implementing similar evaluation pipelines for proprietary models"],"limitations":["Documentation may lag behind code changes — methodology versioning not always synchronized","Exact reproduction requires matching inference hardware and software versions","Some evaluation details (e.g., specific model serving infrastructure) may not be fully documented","Methodology is fixed by Hugging Face — no ability to propose changes or vote on improvements","Documentation is English-only; non-English speakers may struggle to understand evaluation details"],"requires":["GitHub account to access evaluation code repository","Python 3.8+ to run evaluation scripts locally","Understanding of benchmark formats (MMLU JSON, HellaSwag, etc.)","Familiarity with Hugging Face transformers library"],"input_types":["benchmark dataset files (MMLU, HellaSwag, etc.)","model identifiers and inference parameters","evaluation configuration (temperature, max_tokens, etc.)"],"output_types":["evaluation methodology documentation (markdown, PDF)","evaluation code (Python scripts)","exact prompts used for each benchmark","evaluation results with detailed metrics"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_5","uri":"capability://data.processing.analysis.model.metadata.extraction.and.standardization","name":"model-metadata-extraction-and-standardization","description":"Automatically extracts and standardizes metadata from Hugging Face model cards including: parameter count, architecture type, training data, license, quantization support, and context window size. Uses heuristic parsing of model card markdown and Hugging Face API metadata to populate leaderboard columns. Handles missing or inconsistent metadata gracefully with fallback values.","intents":["Quickly identify model specifications (size, architecture, license) without reading full model cards","Filter models by technical specifications (e.g., find all 7B Llama models with Apache 2.0 license)","Understand the relationship between model size and benchmark performance","Identify which models support quantization or have optimized inference implementations"],"best_for":["Engineers making model selection decisions based on technical constraints","Researchers studying scaling laws and architecture trade-offs","Teams building model registries that need standardized metadata","Organizations evaluating licensing and compliance requirements"],"limitations":["Metadata extraction relies on model card consistency — poorly formatted cards may have missing or incorrect data","No validation of metadata accuracy — relies on model developers providing correct information","Some metadata (e.g., training data composition) may be incomplete or proprietary","Context window size may be inferred from model card rather than tested empirically","Quantization support is listed but not tested — actual quantization performance not benchmarked"],"requires":["Model must have a model card on Hugging Face with standard metadata fields","Hugging Face API access to fetch model metadata","Metadata must follow Hugging Face conventions (YAML frontmatter, standard field names)"],"input_types":["Hugging Face model card (markdown with YAML metadata)","model configuration files (config.json, etc.)"],"output_types":["standardized metadata fields (parameter count, architecture, license, etc.)","leaderboard columns with model specifications","filterable metadata for leaderboard search"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_6","uri":"capability://data.processing.analysis.historical.performance.tracking.and.trend.analysis","name":"historical-performance-tracking-and-trend-analysis","description":"Maintains historical snapshots of leaderboard rankings and benchmark scores over time, enabling analysis of model performance trends. Tracks when models enter/exit the leaderboard, how rankings change as new models are released, and performance improvements within model families (e.g., Llama 1 → Llama 2 → Llama 3). Provides time-series visualizations of benchmark score evolution.","intents":["Understand how the open-source LLM landscape is evolving (e.g., are models getting better faster?)","Track a specific model family's performance improvements across versions","Identify inflection points where new architectures or training methods significantly improved performance","Predict future model performance based on historical trends"],"best_for":["Researchers studying LLM progress and scaling trends","Model developers benchmarking their improvements against historical baselines","Organizations making long-term model selection decisions based on trajectory","Analysts tracking the competitive landscape of open-source models"],"limitations":["Historical data only available since leaderboard inception — no pre-existing benchmark history","Benchmark methodology changes over time may make historical comparisons invalid","Models removed from leaderboard (e.g., due to licensing issues) disappear from historical data","Trend analysis is descriptive only — no predictive modeling or forecasting","Time-series data may have gaps if models are not re-evaluated regularly"],"requires":["Leaderboard must have been tracking models for sufficient time period (months to years)","Historical snapshots must be stored and accessible via API or UI","Consistent benchmark methodology across time period for valid comparisons"],"input_types":["historical benchmark scores (time-stamped results)","model release dates and version information","benchmark methodology versions"],"output_types":["time-series charts of benchmark scores","ranking change analysis (how models moved up/down over time)","model family performance trajectories","trend statistics (improvement rate, volatility, etc.)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_7","uri":"capability://data.processing.analysis.benchmark.coverage.analysis.and.gap.identification","name":"benchmark-coverage-analysis-and-gap-identification","description":"Analyzes which capabilities are covered by the benchmark suite and identifies gaps. Provides metadata about each benchmark (what it measures, which model types it favors, known limitations). Highlights models with incomplete evaluations and identifies which benchmarks are most discriminative (highest variance across models). Suggests which additional benchmarks might be valuable to add.","intents":["Understand what capabilities the leaderboard actually measures and what's missing","Identify if a model's strong performance is due to general capability or benchmark-specific optimization","Determine if the benchmark suite is suitable for evaluating models for your specific use case","Advocate for adding benchmarks that measure capabilities important to your domain"],"best_for":["Researchers designing new benchmarks or evaluating benchmark suites","Organizations assessing whether leaderboard rankings are relevant to their use case","Model developers understanding which benchmarks their model is weak on","Teams building domain-specific evaluation suites based on open-source methodology"],"limitations":["Gap analysis is qualitative — no quantitative measure of how important missing capabilities are","Benchmark coverage analysis depends on Hugging Face's assessment — may not reflect community priorities","No mechanism to propose or vote on new benchmarks to add","Analysis is static — doesn't adapt as new benchmarks are added","Doesn't account for benchmark gaming or prompt sensitivity"],"requires":["Documentation of what each benchmark measures","Metadata about benchmark design and known limitations","Statistical analysis of benchmark variance and discriminative power"],"input_types":["benchmark metadata (name, description, task type)","model scores on each benchmark","benchmark design documentation"],"output_types":["benchmark coverage analysis (which capabilities are measured)","gap identification (missing capability areas)","benchmark discriminative power analysis (variance across models)","recommendations for additional benchmarks"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_8","uri":"capability://data.processing.analysis.comparative.model.analysis.and.side.by.side.comparison","name":"comparative model analysis and side-by-side comparison","description":"Enables users to select multiple models and view their performance side-by-side across all benchmarks, with visual comparison charts and difference calculations. The comparison view shows absolute scores, relative performance differences, and highlights areas where models diverge significantly. This is implemented as an interactive UI feature allowing users to add/remove models from comparison and customize visualization (bar charts, radar charts, tables).","intents":["Compare two models I'm deciding between to understand their relative strengths","Analyze performance differences across multiple models to identify patterns","Create comparison visualizations for presentations or documentation","Understand which model is better for specific benchmarks or use cases"],"best_for":["Teams making model selection decisions between shortlisted candidates","Researchers analyzing model performance distributions and outliers","Product managers presenting model options to stakeholders","Practitioners understanding capability tradeoffs between models"],"limitations":["Comparison is limited to models in the leaderboard — can't compare against proprietary models or custom models","No statistical significance testing — differences may be noise rather than meaningful divergence","Comparison doesn't account for inference cost, speed, or resource usage — only accuracy metrics","Limited to predefined visualization types; can't create custom comparison metrics","No ability to weight benchmarks differently or create custom aggregate scores"],"requires":["Web browser with JavaScript for interactive comparison UI","Models must be in the leaderboard"],"input_types":["model identifiers (names or IDs)","benchmark scores for selected models"],"output_types":["side-by-side comparison tables","comparative performance charts (bar, radar, line)","performance difference calculations","summary statistics and insights"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__cap_9","uri":"capability://memory.knowledge.evaluation.methodology.transparency.and.reproducibility.documentation","name":"evaluation methodology transparency and reproducibility documentation","description":"Documents the exact evaluation methodology including benchmark versions, prompt templates, sampling parameters (temperature, top-p, max tokens), and inference framework used. This information is displayed alongside results and made available for download, enabling users to replicate evaluations locally or understand potential sources of variance. The leaderboard maintains version history of evaluation methodology, allowing users to understand how methodology changes have affected scores over time.","intents":["Understand exactly how benchmark scores were computed","Replicate evaluation locally using the same methodology","Identify potential sources of variance or bias in evaluation","Compare results across different evaluation methodologies","Validate that evaluation was conducted fairly and reproducibly"],"best_for":["Researchers validating evaluation methodology and reproducing results","Model developers understanding how their models were evaluated","Practitioners assessing reliability and fairness of benchmark scores","Teams building custom evaluation pipelines based on leaderboard methodology"],"limitations":["Methodology documentation may be incomplete or outdated — changes to evaluation code may not be immediately reflected","Prompt templates and sampling parameters are documented but may not be easily downloadable or version-controlled","No information about evaluation infrastructure (hardware, batch size, number of runs) that could affect reproducibility","Doesn't include information about evaluation failures or edge cases — only successful evaluations are reported","Methodology changes over time may make historical results incomparable"],"requires":["Web browser to view methodology documentation","Optional: Python environment to replicate evaluation locally"],"input_types":["evaluation configuration (prompts, parameters, benchmark versions)","inference framework and settings","evaluation methodology documentation"],"output_types":["methodology documentation (text, JSON)","prompt templates and sampling parameters","evaluation code and scripts","methodology version history"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"open-llm-leaderboard__headline","uri":"capability://testing.quality.open.source.llm.benchmarking.platform","name":"open-source llm benchmarking platform","description":"A comprehensive leaderboard for evaluating open-source large language models (LLMs) against standardized benchmarks, providing a reference for model comparison in the AI community.","intents":["best open-source LLM benchmark","open-source LLM evaluation for research","compare open-source LLMs","top-performing open-source LLMs","open-source LLM leaderboard"],"best_for":["researchers","developers","AI enthusiasts"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":62,"verified":false,"data_access_risk":"high","permissions":["Model must be published to Hugging Face Model Hub or have public API endpoint","Model must support text generation (no vision-only or embedding-only models)","Internet connectivity to access evaluation infrastructure","No local setup required — evaluation runs on Hugging Face infrastructure","Models must have completed at least some subset of benchmarks to appear in rankings","Benchmark evaluation infrastructure (same as standardized-benchmark-evaluation-pipeline)","Hugging Face account with model publishing permissions","Model must be in a supported format (transformers, safetensors, etc.)","Model card with proper metadata (architecture, training data, license)","Web browser with JavaScript enabled"],"failure_modes":["Benchmarks are static snapshots — don't capture real-world performance on domain-specific tasks","Evaluation methodology may not reflect how models perform with different prompting strategies or system prompts","Models must be hosted on Hugging Face Model Hub or accessible via API — private/local models cannot be evaluated","Benchmark suite is English-only; multilingual performance not captured","Evaluation latency means leaderboard updates lag behind model releases by hours to days","Aggregation weights are fixed by Hugging Face — no customization for domain-specific priorities","Missing benchmark results for some models can skew rankings if aggregation doesn't handle sparse data well","Equally weights all benchmarks regardless of their relevance to specific use cases (e.g., code generation not benchmarked)","Does not account for inference cost, latency, or model size — only accuracy metrics","Evaluation queue can have significant latency during high-submission periods (hours to days)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=open-llm-leaderboard","compare_url":"https://unfragile.ai/compare?artifact=open-llm-leaderboard"}},"signature":"OhnHobKLe/boEa9mHWDVgXDK65r550TWSP8EC2kL43q/OkLYZOeGE9E3kNM6IFcgr2GqXYZTVOsqdglaGvUrAw==","signedAt":"2026-06-20T19:00:11.050Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/open-llm-leaderboard","artifact":"https://unfragile.ai/open-llm-leaderboard","verify":"https://unfragile.ai/api/v1/verify?slug=open-llm-leaderboard","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}