{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"livebench","slug":"livebench","name":"LiveBench","type":"benchmark","url":"https://livebench.ai","page_url":"https://unfragile.ai/livebench","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"livebench__cap_0","uri":"capability://data.processing.analysis.contamination.free.benchmark.dataset.curation.with.continuous.updates","name":"contamination-free benchmark dataset curation with continuous updates","description":"Automatically ingests questions from recent information sources (news, research papers, current events) with temporal filtering to ensure test data was not published before model training cutoffs, preventing data leakage. Uses publication date verification and source freshness validation to guarantee benchmark questions are genuinely novel and not present in training corpora.","intents":["Evaluate LLM performance on truly unseen information without contamination risk","Track model capability degradation over time as new information emerges","Identify which models have been trained on benchmark data by comparing performance on fresh vs. stale questions"],"best_for":["LLM researchers validating model generalization on current information","Organizations comparing multiple LLM providers without contamination concerns","Model developers ensuring their training data doesn't overlap with evaluation sets"],"limitations":["Requires reliable publication date metadata from sources — unreliable timestamps can introduce contamination","Cannot retroactively verify if models were trained on data after their official cutoff date","Limited to domains with clear publication dates (excludes some proprietary or internal knowledge)"],"requires":["Access to LiveBench API or web interface","LLM API keys (OpenAI, Anthropic, or other supported providers) to submit model responses","Understanding of model training cutoff dates for proper interpretation"],"input_types":["LLM model identifiers","API credentials for model providers"],"output_types":["Benchmark scores","Per-question performance metrics","Contamination risk assessment"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_1","uri":"capability://data.processing.analysis.multi.domain.llm.capability.evaluation.across.math.coding.reasoning.language.and.data.analysis","name":"multi-domain llm capability evaluation across math, coding, reasoning, language, and data analysis","description":"Orchestrates evaluation across five distinct capability domains using domain-specific question formats and scoring rubrics. Each domain uses tailored evaluation logic: math uses numerical accuracy checking, coding uses execution-based validation, reasoning uses logical consistency scoring, language uses semantic similarity metrics, and data analysis uses output format and correctness validation.","intents":["Measure which capability areas a model excels or struggles in with granular domain-level scores","Compare models across balanced capability dimensions rather than overall accuracy","Identify capability gaps to guide model selection for specific use cases (e.g., code generation vs. reasoning)"],"best_for":["Model researchers analyzing capability profiles across different LLM architectures","Teams selecting models for specific applications requiring particular strengths","Benchmark designers studying how different domains correlate in model performance"],"limitations":["Domain-specific scoring may not capture cross-domain reasoning that combines multiple capabilities","Weighting between domains is fixed rather than customizable per use case","Some questions may test multiple domains simultaneously, making attribution ambiguous"],"requires":["Models capable of generating text responses in all five domains","Domain-specific evaluation infrastructure (math solvers, code execution sandboxes, semantic similarity models)"],"input_types":["Domain-specific questions (mathematical problems, code tasks, reasoning puzzles, language tasks, data analysis queries)"],"output_types":["Domain-level scores (0-100 per domain)","Aggregate benchmark score","Per-question correctness labels"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_2","uri":"capability://data.processing.analysis.real.time.benchmark.result.aggregation.and.leaderboard.generation","name":"real-time benchmark result aggregation and leaderboard generation","description":"Collects model evaluation results from submitted runs, aggregates scores across questions and domains, and generates live leaderboards ranked by overall and domain-specific performance. Uses incremental aggregation to update rankings as new model submissions arrive without requiring full recomputation.","intents":["View current model rankings across all evaluated models in real-time","Track how model performance changes as new benchmark questions are added","Compare model scores side-by-side with filtering by domain or other criteria"],"best_for":["Model developers monitoring their model's competitive position","Researchers comparing published models on a single standardized benchmark","Organizations selecting models based on current leaderboard rankings"],"limitations":["Leaderboard rankings can be gamed if submission process lacks authentication or rate limiting","Aggregation assumes all models are evaluated on identical question sets — partial evaluations may skew rankings","Real-time updates may show incomplete results if models are still being evaluated"],"requires":["Web interface or API access to LiveBench","Model evaluation results submitted in standardized format"],"input_types":["Model evaluation results (model name, domain scores, question-level correctness)"],"output_types":["Leaderboard rankings (JSON, HTML table, or API response)","Domain-specific rankings","Historical score trends"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_3","uri":"capability://data.processing.analysis.automated.question.generation.and.sourcing.from.recent.information.feeds","name":"automated question generation and sourcing from recent information feeds","description":"Continuously monitors and ingests questions from recent publications, news sources, research papers, and other current information feeds using automated extraction pipelines. Filters ingested content by publication date, relevance to benchmark domains, and question quality metrics before adding to the active benchmark pool.","intents":["Ensure benchmark questions are always fresh and sourced from recent, verifiable information","Automatically expand benchmark size without manual question authoring","Maintain domain coverage across math, coding, reasoning, language, and data analysis as new information emerges"],"best_for":["Benchmark maintainers wanting to scale question pools without manual curation","Researchers studying how model performance changes as new information becomes available","Organizations needing continuously updated evaluation datasets"],"limitations":["Automated extraction may introduce low-quality or ambiguous questions that require manual filtering","Source diversity is limited to feeds with structured publication metadata","Question distribution across domains may be unbalanced if sources have domain-specific biases","Extraction errors (e.g., incomplete questions, corrupted formatting) require post-processing"],"requires":["Access to information feeds (news APIs, research paper repositories, etc.)","Question quality filtering model or heuristics","Domain classification model to assign questions to capability areas"],"input_types":["News articles, research papers, web content with publication dates"],"output_types":["Structured questions with domain labels, source attribution, and publication dates","Question quality scores"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_4","uri":"capability://tool.use.integration.model.response.submission.and.evaluation.pipeline.with.standardized.formats","name":"model response submission and evaluation pipeline with standardized formats","description":"Accepts model responses submitted via API or web interface in standardized formats, validates response structure and content, routes responses to domain-specific evaluators, and records results with metadata (submission timestamp, model version, evaluator version). Supports batch submission for efficient evaluation of multiple models.","intents":["Submit model responses for evaluation against benchmark questions","Integrate LiveBench evaluation into model development pipelines and CI/CD workflows","Evaluate multiple model versions or providers in batch without manual submission per model"],"best_for":["Model developers integrating LiveBench into automated evaluation workflows","Teams comparing multiple LLM providers on a single benchmark","Researchers running large-scale model evaluation studies"],"limitations":["Submission format must match expected schema — malformed submissions are rejected without detailed error messages","Batch submission may have rate limits to prevent benchmark abuse","Evaluation latency depends on evaluator availability and queue depth","No built-in retry logic for failed evaluations — requires manual resubmission"],"requires":["API key or authentication token for LiveBench","Model responses in standardized JSON format","Network connectivity to LiveBench API endpoint"],"input_types":["Model responses (text, code, structured data depending on domain)","Model metadata (name, version, provider)"],"output_types":["Evaluation results (correctness labels, scores, domain-specific metrics)","Submission confirmation with result tracking ID"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_5","uri":"capability://data.processing.analysis.domain.specific.evaluation.logic.with.execution.based.and.semantic.validation","name":"domain-specific evaluation logic with execution-based and semantic validation","description":"Implements specialized evaluators for each capability domain: code evaluator executes submissions in sandboxed environments and checks output correctness, math evaluator performs numerical comparison with tolerance handling, reasoning evaluator validates logical consistency, language evaluator uses semantic similarity metrics, and data analysis evaluator checks output format and data accuracy. Each evaluator is independently versioned and can be updated without affecting others.","intents":["Accurately score model responses using domain-appropriate evaluation methods","Detect partial correctness in code (e.g., correct logic but wrong output format)","Handle numerical precision issues in math evaluation with configurable tolerances"],"best_for":["Benchmark designers needing domain-specific evaluation beyond simple string matching","Researchers studying how evaluation methodology affects model rankings","Teams requiring fine-grained correctness assessment across multiple capability areas"],"limitations":["Code execution in sandboxes may timeout on inefficient solutions, penalizing correct but slow implementations","Semantic similarity metrics for language evaluation may incorrectly score paraphrases as incorrect","Numerical tolerance thresholds in math evaluation are fixed rather than question-specific","Reasoning evaluation relies on heuristics and may miss valid alternative logical approaches"],"requires":["Sandboxed code execution environment (Docker, WebAssembly, or similar) for code evaluation","Semantic similarity model (e.g., sentence transformers) for language evaluation","Domain-specific evaluation libraries (math solvers, data validation tools)"],"input_types":["Model responses in domain-specific formats (code snippets, numerical answers, text explanations, data outputs)"],"output_types":["Correctness labels (correct/incorrect/partial)","Domain-specific scores (execution time for code, numerical error for math, similarity score for language)","Detailed evaluation feedback"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_6","uri":"capability://data.processing.analysis.temporal.metadata.tracking.and.contamination.risk.reporting","name":"temporal metadata tracking and contamination risk reporting","description":"Records publication dates, source URLs, and model training cutoff dates for all benchmark questions and submissions. Generates contamination risk reports by comparing question publication dates against model training cutoffs, flagging potential data leakage when questions were published before training data collection ended. Provides transparency into which results are reliable based on temporal alignment.","intents":["Identify which benchmark results are contamination-free based on temporal alignment","Understand contamination risk for each model-question pair","Make informed decisions about model selection based on uncontaminated performance only"],"best_for":["Researchers requiring contamination-free evaluation results","Organizations comparing models where some may have been trained on benchmark data","Benchmark designers studying the impact of data contamination on model rankings"],"limitations":["Relies on accurate training cutoff dates from model providers — dates may be approximate or misleading","Cannot detect if models were fine-tuned on benchmark data after initial training","Publication date metadata may be unreliable for some sources (e.g., updated articles with old publication dates)","Contamination risk is binary (before/after cutoff) rather than probabilistic"],"requires":["Accurate model training cutoff dates (from model providers or documentation)","Reliable publication date metadata for all benchmark sources"],"input_types":["Model training cutoff dates","Question publication dates and source URLs"],"output_types":["Contamination risk reports (per model, per question, aggregate)","Filtered leaderboards showing only uncontaminated results","Temporal alignment visualizations"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__cap_7","uri":"capability://automation.workflow.open.source.benchmark.infrastructure.and.reproducibility.support","name":"open-source benchmark infrastructure and reproducibility support","description":"Publishes benchmark questions, evaluation code, and leaderboard data as open-source artifacts, enabling external researchers to reproduce results, audit evaluation logic, and extend the benchmark. Provides version control for questions and evaluators, allowing tracking of changes and reproducibility across benchmark versions.","intents":["Audit benchmark evaluation logic to ensure fairness and correctness","Reproduce published results using open-source question sets and evaluators","Extend benchmark with custom questions or evaluation methods"],"best_for":["Researchers requiring transparent, auditable benchmark methodology","Organizations building custom benchmarks based on LiveBench infrastructure","Teams needing reproducible evaluation across multiple environments"],"limitations":["Open-source release may lag behind live benchmark updates","Reproducing results requires setting up evaluation infrastructure (code execution sandboxes, semantic models)","Custom extensions may diverge from official benchmark, making comparisons difficult","No official support for custom evaluation logic — users must maintain their own forks"],"requires":["Git and version control knowledge to access and track benchmark versions","Development environment matching benchmark requirements (Python, specific libraries)","Understanding of benchmark evaluation methodology to properly extend or modify"],"input_types":["Benchmark question sets (JSON or similar format)","Evaluation code (Python scripts or similar)"],"output_types":["Open-source repositories with questions and evaluators","Documentation for reproduction and extension","Version history and change logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"livebench__headline","uri":"capability://testing.quality.contamination.free.llm.benchmarking.tool","name":"contamination-free llm benchmarking tool","description":"LiveBench is a unique benchmarking tool for large language models that ensures contamination-free evaluations by continuously updating with new questions from recent information sources, making it ideal for assessing math, coding, reasoning, language, and data analysis capabilities.","intents":["best LLM benchmarking tool","LLM benchmark for contamination-free testing","top tools for evaluating language models","continuous LLM evaluation solutions","how to benchmark language models without data leakage"],"best_for":["researchers","developers","data scientists"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["Access to LiveBench API or web interface","LLM API keys (OpenAI, Anthropic, or other supported providers) to submit model responses","Understanding of model training cutoff dates for proper interpretation","Models capable of generating text responses in all five domains","Domain-specific evaluation infrastructure (math solvers, code execution sandboxes, semantic similarity models)","Web interface or API access to LiveBench","Model evaluation results submitted in standardized format","Access to information feeds (news APIs, research paper repositories, etc.)","Question quality filtering model or heuristics","Domain classification model to assign questions to capability areas"],"failure_modes":["Requires reliable publication date metadata from sources — unreliable timestamps can introduce contamination","Cannot retroactively verify if models were trained on data after their official cutoff date","Limited to domains with clear publication dates (excludes some proprietary or internal knowledge)","Domain-specific scoring may not capture cross-domain reasoning that combines multiple capabilities","Weighting between domains is fixed rather than customizable per use case","Some questions may test multiple domains simultaneously, making attribution ambiguous","Leaderboard rankings can be gamed if submission process lacks authentication or rate limiting","Aggregation assumes all models are evaluated on identical question sets — partial evaluations may skew rankings","Real-time updates may show incomplete results if models are still being evaluated","Automated extraction may introduce low-quality or ambiguous questions that require manual filtering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"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":"2026-05-24T12:16:23.327Z","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=livebench","compare_url":"https://unfragile.ai/compare?artifact=livebench"}},"signature":"JXtL9v/wJbbCqwZ5ZrXWaXgiiafFFvjPzOGzNuDXGbdoB8wcEpSs/WwBDRwoY+0oK3FP/+TOp5nlilj1Zs0sBQ==","signedAt":"2026-06-21T14:47:47.133Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/livebench","artifact":"https://unfragile.ai/livebench","verify":"https://unfragile.ai/api/v1/verify?slug=livebench","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"}}