{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-jeinlee1991--chinese-llm-benchmark","slug":"jeinlee1991--chinese-llm-benchmark","name":"chinese-llm-benchmark","type":"benchmark","url":"https://nonelinear.com","page_url":"https://unfragile.ai/jeinlee1991--chinese-llm-benchmark","categories":["testing-quality"],"tags":["agentic-ai","artificial-intelligence","llm-agent","llm-evaluation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_0","uri":"capability://data.processing.analysis.multi.domain.llm.performance.evaluation.across.8.specialized.domains","name":"multi-domain llm performance evaluation across 8 specialized domains","description":"Evaluates Chinese LLMs across 8 major domains (Medical, Education, Finance, Law, Administrative Affairs, Psychological Health, Reasoning & Math, Language & Instruction Following) using approximately 300 specific evaluation dimensions. Each domain assessment aggregates task-specific scores (1-5 scale per question) normalized to 0-100 point scale, then combines domain scores to produce overall model rankings. The framework uses domain-specific test questions designed to measure real-world capability rather than general language understanding.","intents":["Compare performance of 298+ LLMs across specialized knowledge domains to select models for domain-specific applications","Identify which models excel in medical reasoning, financial analysis, legal knowledge, or mathematical problem-solving","Benchmark commercial vs open-source models within the same capability tier to make cost-performance tradeoffs","Track model improvement over time by re-evaluating against consistent domain-specific test suites"],"best_for":["ML researchers and practitioners evaluating Chinese LLM suitability for domain-specific applications","Organizations selecting models for regulated industries (healthcare, finance, legal) requiring domain expertise validation","Model developers benchmarking improvements against 298 competing models across standardized dimensions"],"limitations":["Evaluation methodology and scoring rubrics not fully transparent in public documentation — difficult to reproduce exact scores","Domain coverage limited to 8 major areas; specialized domains (robotics, chemistry, biology) not separately evaluated","Evaluation frequency and update cadence not specified — leaderboard staleness risk for rapidly evolving models","No per-sample error analysis or failure mode categorization within domains — only aggregate scores provided"],"requires":["Access to ReLE evaluation framework (open-source, available on GitHub)","Model API access or local deployment capability for models being evaluated","Chinese language proficiency to interpret domain-specific test questions and results"],"input_types":["LLM model identifiers (name, version, provider)","Domain-specific test questions in Chinese (medical cases, financial scenarios, legal questions, etc.)","Model responses to evaluation prompts"],"output_types":["Numerical scores (0-100) per domain","Overall composite score (average across 8 domains)","Ranked leaderboard position within model category (commercial/open-source, price tier, parameter size)","Domain-specific performance breakdown"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_1","uri":"capability://data.processing.analysis.multi.tier.model.leaderboard.organization.with.category.based.filtering","name":"multi-tier model leaderboard organization with category-based filtering","description":"Organizes 298 evaluated models into hierarchical leaderboards using primary classification (commercial vs open-source) and secondary tiers (price tier for commercial models, parameter size for open-source models). The system maintains separate ranked lists for each category, enabling users to compare models within similar cost/capability profiles. Leaderboard data is stored in markdown files (commerce2.md, reasonmodel.md, alldata.md) with model metadata (name, version, provider, parameters, pricing) and performance scores aggregated from domain evaluations.","intents":["Find the best-performing model within a specific budget tier (e.g., cheapest models under $0.01/1K tokens)","Compare open-source models of similar parameter size to identify efficiency leaders","Identify commercial model alternatives at different price points with comparable performance","Discover reasoning-specialized models ranked separately from general-purpose models"],"best_for":["Teams with budget constraints needing to identify best-value models within cost tiers","Developers choosing between open-source models with similar parameter counts","Organizations evaluating commercial model subscriptions with price-performance analysis"],"limitations":["Leaderboard tiers are static categories — no dynamic tier assignment based on real-time pricing changes","Price tier definitions not explicitly documented — unclear how models are assigned to cost brackets","Parameter size data may be outdated for frequently-updated models","No leaderboard filtering by inference speed, latency, or throughput — only ranking by evaluation scores"],"requires":["Access to leaderboard markdown files in repository","Model metadata including pricing, parameter count, and provider information","Evaluation scores from multi-domain assessment capability"],"input_types":["Model metadata (name, version, provider, parameters, pricing)","Domain evaluation scores (0-100 per domain)","Model classification (commercial/open-source, category tier)"],"output_types":["Ranked leaderboard lists (markdown format)","Model comparison tables with scores and metadata","Category-filtered rankings (e.g., 'best open-source models under 7B parameters')"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_10","uri":"capability://memory.knowledge.model.metadata.management.and.comprehensive.model.information.system","name":"model metadata management and comprehensive model information system","description":"Maintains comprehensive metadata for 298+ evaluated models including name, version, provider/developer organization, model type (commercial/open-source), parameter count, pricing information, release date, and availability status. Metadata is stored alongside evaluation scores in leaderboard files and enables filtering, sorting, and comparison based on model attributes. The system tracks model evolution (versions, updates) and maintains historical metadata for deprecated or superseded models.","intents":["Filter models by specific attributes (provider, parameter size, pricing tier, availability)","Track model versions and identify which version was evaluated","Compare models from same provider to identify best performer","Maintain accurate model information for production deployment decisions"],"best_for":["Teams building model selection tools requiring comprehensive model metadata","Researchers analyzing model landscape by provider, size, or pricing","Organizations tracking model versions for reproducibility and audit trails","Developers maintaining model registries or catalogs"],"limitations":["Metadata update frequency not specified — pricing and availability data may become stale","No structured metadata format documented — metadata embedded in markdown leaderboards rather than structured database","Version tracking incomplete — unclear if all model versions are tracked or only latest versions","No metadata validation or quality assurance process documented"],"requires":["Model metadata collection and curation process (manual or automated)","Leaderboard markdown files containing model information","Version control system (Git) for tracking metadata changes"],"input_types":["Model name, version, and provider information","Model type classification (commercial/open-source)","Parameter count and model size","Pricing information (API costs or deployment requirements)","Release date and availability status"],"output_types":["Model metadata records with all attributes","Filtered model lists by attribute (provider, size, price tier)","Model comparison tables with metadata and scores","Model version history and evolution tracking"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_2","uri":"capability://data.processing.analysis.defect.library.indexing.and.error.pattern.analysis.across.2m.model.failures","name":"defect library indexing and error pattern analysis across 2m+ model failures","description":"Maintains a defect library containing over 2 million documented model errors collected during evaluation across all domains and models. The system indexes failures by model, domain, question type, and error category, enabling researchers to identify systematic failure patterns. Defect records link specific model errors to evaluation questions, domain context, and error classification, supporting root-cause analysis and model improvement research. The library serves as a queryable knowledge base for understanding model weaknesses rather than just performance scores.","intents":["Identify systematic failure patterns in a specific model (e.g., 'Claude-4 fails on medical reasoning questions involving drug interactions')","Analyze domain-specific error distributions to understand which domains are hardest across all models","Find models with similar error profiles to understand capability gaps in the LLM landscape","Extract failure examples for model fine-tuning or adversarial testing datasets"],"best_for":["Model developers analyzing failure modes to guide fine-tuning or RLHF improvements","Researchers studying systematic weaknesses in Chinese LLM capabilities","Teams building domain-specific model selection tools that need to understand failure patterns","Safety researchers identifying adversarial or edge-case failure modes"],"limitations":["Defect library structure and query interface not documented — unclear how to programmatically access 2M+ errors","Error classification taxonomy not specified — unknown how failures are categorized (hallucination, reasoning error, knowledge gap, etc.)","No temporal tracking of defects — unclear if library tracks when errors were introduced or fixed across model versions","Privacy/licensing unclear for using defect examples in derivative research or fine-tuning datasets"],"requires":["Access to defect library database or export (format/API not specified in documentation)","Model identifiers and evaluation question IDs to cross-reference errors","Understanding of error classification scheme used in library"],"input_types":["Model identifiers (name, version)","Domain context (Medical, Finance, Law, etc.)","Evaluation question IDs or content","Error classification tags"],"output_types":["Defect records with model, domain, question, and error details","Error pattern summaries (e.g., '45% of medical errors involve drug interaction reasoning')","Failure example datasets for analysis or fine-tuning","Model-to-model error similarity matrices"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_3","uri":"capability://text.generation.language.chinese.language.specific.evaluation.with.gaokao.level.academic.assessment","name":"chinese language-specific evaluation with gaokao-level academic assessment","description":"Implements specialized evaluation for Chinese language understanding and instruction following, including Gaokao (Chinese college entrance exam) level questions that test reading comprehension, writing quality, and complex reasoning in Chinese. The evaluation framework includes domain-specific language tasks (medical terminology understanding, legal document interpretation, financial report analysis) alongside general Chinese language proficiency assessment. Scoring incorporates both accuracy and response quality (1-5 scale) to capture nuanced language performance beyond binary correctness.","intents":["Evaluate LLM capability to handle complex Chinese language tasks at academic/professional level","Assess models for Chinese content generation applications (writing, summarization, translation)","Benchmark instruction-following ability in Chinese-specific contexts with cultural/linguistic nuances","Identify models suitable for Chinese educational or professional applications requiring high language quality"],"best_for":["Chinese organizations deploying LLMs for content generation, customer service, or knowledge work","Researchers studying Chinese language model capabilities vs English-centric models","Educational institutions evaluating models for student assistance or tutoring applications","Teams building Chinese-language AI products requiring high language quality standards"],"limitations":["Gaokao-level assessment methodology not detailed — unclear how questions are selected, difficulty calibrated, or scoring rubrics applied","Language quality scoring (1-5 scale) is subjective — no inter-rater reliability metrics or scoring guidelines published","Evaluation limited to Simplified Chinese — no Traditional Chinese or regional dialect assessment","No fine-grained language error categorization (grammar, vocabulary, style, coherence) — only aggregate language scores"],"requires":["Chinese language proficiency to interpret test questions and evaluate response quality","Access to Gaokao question bank or equivalent academic Chinese assessment dataset","Human raters or automated scoring system for language quality assessment (1-5 scale)"],"input_types":["Chinese language test questions (reading comprehension, writing prompts, instruction-following tasks)","LLM responses in Chinese","Domain context (general language, medical Chinese, legal Chinese, etc.)"],"output_types":["Language proficiency scores (0-100 per language domain)","Gaokao-level performance metrics","Instruction-following accuracy in Chinese contexts","Response quality ratings (1-5 scale) with qualitative feedback"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_4","uri":"capability://planning.reasoning.mathematical.reasoning.and.logic.problem.evaluation.with.specialized.scoring","name":"mathematical reasoning and logic problem evaluation with specialized scoring","description":"Evaluates models on mathematical computation, logical reasoning, and complex problem-solving through domain-specific test questions in the 'Reasoning & Math' category. The evaluation framework assesses both correctness of final answers and quality of reasoning steps (1-5 scale), capturing partial credit for correct methodology with computational errors. Supports multi-step reasoning problems, symbolic manipulation, and logical inference tasks designed to test mathematical capability beyond simple arithmetic.","intents":["Identify models suitable for mathematical tutoring, homework assistance, or STEM education applications","Benchmark reasoning capability to select models for complex problem-solving tasks","Evaluate models for scientific research support requiring mathematical accuracy and logical rigor","Compare mathematical reasoning quality across models to identify reasoning-specialized variants"],"best_for":["Educational technology companies building math tutoring or homework help systems","Research teams using LLMs for scientific computation or theoretical problem-solving","Organizations deploying models for financial modeling or quantitative analysis","Developers of reasoning-specialized models benchmarking improvements"],"limitations":["Scoring methodology for partial credit not documented — unclear how reasoning quality (1-5 scale) maps to mathematical correctness","No distinction between arithmetic errors, conceptual misunderstandings, and logical fallacies in error analysis","Mathematical domain coverage not specified — unclear if evaluation includes calculus, linear algebra, probability, or only basic arithmetic","No symbolic math verification — relies on model text output rather than formal proof checking or symbolic computation"],"requires":["Mathematical test question dataset with correct answers and solution steps","Scoring rubric for evaluating reasoning quality and partial credit","Human raters or automated scoring system for 1-5 quality assessment"],"input_types":["Mathematical problems (arithmetic, algebra, geometry, logic puzzles)","Multi-step reasoning problems","LLM responses with reasoning steps and final answers"],"output_types":["Mathematical reasoning scores (0-100)","Correctness metrics (accuracy of final answers)","Reasoning quality ratings (1-5 scale)","Error categorization (computational vs conceptual errors)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_5","uri":"capability://data.processing.analysis.professional.domain.specific.knowledge.evaluation.medical.finance.law.administrative","name":"professional domain-specific knowledge evaluation (medical, finance, law, administrative)","description":"Implements specialized evaluation across four professional domains (Medical, Finance, Law, Administrative Affairs) with domain-expert-designed test questions requiring specialized knowledge and reasoning. Each domain assessment uses realistic scenarios (medical case studies, financial analysis problems, legal document interpretation, administrative policy questions) to evaluate practical professional capability rather than general knowledge. Scoring incorporates domain-specific rubrics reflecting professional standards and best practices in each field.","intents":["Select models for professional applications (medical documentation, financial advisory, legal research, government services)","Evaluate models for compliance with domain-specific knowledge requirements in regulated industries","Benchmark professional-grade models against general-purpose models to justify specialized model selection","Identify models suitable for domain-specific fine-tuning or RAG augmentation"],"best_for":["Healthcare organizations evaluating models for clinical decision support or medical documentation","Financial institutions assessing models for investment analysis, risk assessment, or financial advisory","Law firms and legal tech companies selecting models for legal research and document analysis","Government agencies evaluating models for policy analysis and administrative decision support"],"limitations":["Domain expertise requirements for test question design and scoring — unclear if domain experts reviewed all questions","No distinction between knowledge-based errors and reasoning errors within domains","Regulatory compliance validation not addressed — models may pass evaluation but fail regulatory requirements","No domain-specific error analysis — unclear which sub-domains (e.g., cardiology vs oncology in medical) are weak"],"requires":["Domain-expert-designed test questions reflecting professional standards","Domain-specific scoring rubrics aligned with professional best practices","Human raters with domain expertise for quality assessment","Understanding of domain-specific terminology and context"],"input_types":["Professional domain test questions (medical cases, financial scenarios, legal documents, administrative policies)","LLM responses to domain-specific prompts","Domain context and professional standards"],"output_types":["Domain-specific knowledge scores (0-100 per domain)","Professional capability ratings (1-5 scale)","Domain-specific error analysis","Suitability assessment for professional applications"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_6","uri":"capability://text.generation.language.psychological.health.and.mental.health.knowledge.assessment","name":"psychological health and mental health knowledge assessment","description":"Evaluates models on psychological health concepts, mental health counseling knowledge, and psychological reasoning through specialized test questions in the 'Psychological Health' domain. Assessment covers mental health terminology, therapeutic approaches, psychological assessment, and ethical counseling practices. Scoring incorporates both knowledge accuracy and quality of psychological reasoning (1-5 scale) to evaluate capability for mental health support applications.","intents":["Evaluate models for mental health chatbot or counseling support applications","Assess models for psychological content generation (mental health education, wellness resources)","Benchmark psychological knowledge to identify models suitable for mental health professional support tools","Identify safety risks in models used for mental health applications"],"best_for":["Mental health technology companies building chatbots or digital therapeutics","Healthcare organizations deploying models for patient education or mental health support","Researchers studying AI capability in mental health domains","Safety teams evaluating risks of models in sensitive mental health applications"],"limitations":["Psychological assessment methodology not detailed — unclear if evaluation includes ethical considerations or harm prevention","No distinction between knowledge accuracy and therapeutic appropriateness — models may pass knowledge test but give harmful advice","Licensing/credential requirements not addressed — unclear if models are evaluated for compliance with mental health professional standards","No evaluation of crisis response capability or safety guardrails for mental health applications"],"requires":["Psychological health test questions designed by mental health professionals","Scoring rubrics reflecting psychological knowledge standards and ethical practices","Human raters with psychology/mental health expertise","Understanding of mental health terminology and therapeutic approaches"],"input_types":["Psychological health test questions (mental health scenarios, therapeutic questions, psychological assessment cases)","LLM responses to mental health prompts","Mental health domain context"],"output_types":["Psychological health knowledge scores (0-100)","Mental health reasoning quality ratings (1-5 scale)","Suitability assessment for mental health applications","Safety risk indicators"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_7","uri":"capability://automation.workflow.real.time.leaderboard.updates.and.continuous.model.evaluation.pipeline","name":"real-time leaderboard updates and continuous model evaluation pipeline","description":"Implements continuous evaluation pipeline that regularly re-evaluates models and updates leaderboards with new results, maintaining 'Really Reliable Live Evaluation' (ReLE) as the system name indicates. The pipeline processes new model versions, newly released models, and periodic re-evaluation of existing models to keep rankings current. Updates are published to markdown leaderboard files (commerce2.md, reasonmodel.md, alldata.md) enabling version-controlled tracking of ranking changes over time.","intents":["Monitor how model rankings change over time as new versions are released","Identify rapidly improving models that may represent better value than previously-ranked alternatives","Track when new models enter the evaluation system and their initial performance","Maintain current leaderboard data for production model selection decisions"],"best_for":["Organizations making ongoing model selection decisions requiring current performance data","Researchers tracking LLM capability evolution over time","Model developers monitoring competitive positioning against other models","Teams building model selection tools that need fresh leaderboard data"],"limitations":["Update frequency and schedule not documented — unclear how often leaderboards are refreshed","No API or programmatic access to leaderboard updates — requires manual GitHub polling or RSS monitoring","Historical leaderboard snapshots not maintained — difficult to analyze ranking changes over time","No notification system for significant ranking changes or new model additions"],"requires":["Automated evaluation pipeline infrastructure (not publicly documented)","Access to new model versions and APIs for continuous evaluation","Git repository access to publish updated leaderboard files","Computational resources for regular model evaluation"],"input_types":["New model versions and releases","Updated model metadata (pricing, parameters, availability)","Evaluation test questions and datasets"],"output_types":["Updated leaderboard markdown files with new rankings","Timestamp metadata for leaderboard update dates","Version control history of ranking changes","New model entries with initial evaluation scores"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_8","uri":"capability://data.processing.analysis.commercial.vs.open.source.model.comparison.with.price.performance.analysis","name":"commercial vs open-source model comparison with price-performance analysis","description":"Enables direct comparison between commercial models (ChatGPT, Claude, Gemini, Qwen, etc.) and open-source models (DeepSeek, Llama, Phi, etc.) by organizing leaderboards with separate commercial and open-source tiers. Commercial models are further categorized by pricing tier (e.g., ultra-cheap, standard, premium), while open-source models are categorized by parameter size (7B, 13B, 70B, etc.). This structure enables price-performance analysis comparing commercial API costs against open-source deployment costs.","intents":["Evaluate whether expensive commercial models justify their cost vs cheaper open-source alternatives","Identify best-value open-source models within parameter size constraints","Compare commercial model pricing tiers to find cost-optimal options","Make build-vs-buy decisions by comparing commercial API costs against open-source deployment infrastructure"],"best_for":["Cost-conscious teams evaluating LLM deployment options with budget constraints","Organizations comparing commercial API subscriptions vs self-hosted open-source models","Teams with specific infrastructure constraints (on-premise, edge deployment) evaluating open-source options","Startups optimizing unit economics by selecting models based on price-performance ratio"],"limitations":["Price tier definitions not documented — unclear how commercial models are assigned to cost brackets","Open-source deployment costs not included in analysis — comparison only includes model capability, not infrastructure costs","Inference speed and latency not factored into price-performance analysis — only evaluation scores used","Commercial pricing changes frequently — leaderboard pricing data may become stale quickly"],"requires":["Current pricing data for commercial models (API costs per token)","Parameter count and model size data for open-source models","Evaluation scores from multi-domain assessment","Categorization logic for assigning models to price/size tiers"],"input_types":["Model type (commercial vs open-source)","Pricing information (API costs for commercial, parameter size for open-source)","Evaluation scores (0-100 per domain)","Model metadata (provider, version, availability)"],"output_types":["Separate leaderboards for commercial and open-source models","Price-tier-specific rankings (e.g., 'best models under $0.01/1K tokens')","Parameter-size-specific rankings (e.g., 'best 7B parameter models')","Price-performance comparison tables"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-jeinlee1991--chinese-llm-benchmark__cap_9","uri":"capability://planning.reasoning.reasoning.specialized.model.identification.and.separate.ranking","name":"reasoning-specialized model identification and separate ranking","description":"Identifies and separately ranks models with specialized reasoning capabilities (e.g., DeepSeek-R1, o1-mini, reasoning-optimized variants) in dedicated leaderboard (reasonmodel.md). These models are evaluated on the same domain tasks but ranked separately to highlight their specialized reasoning strengths. The system recognizes that reasoning-specialized models may have different performance profiles (stronger on math/logic, potentially weaker on general knowledge) and enables comparison within the reasoning-specialist category.","intents":["Identify reasoning-specialized models suitable for complex problem-solving, mathematical reasoning, or logical inference tasks","Compare reasoning-specialized models against each other to find best reasoning capability","Evaluate whether reasoning specialization justifies additional cost or latency vs general-purpose models","Benchmark reasoning capability improvements in new model versions"],"best_for":["Teams building applications requiring advanced reasoning (scientific research, mathematical problem-solving, complex logic)","Researchers studying reasoning capability in LLMs","Organizations evaluating whether reasoning-specialized models justify their typically higher cost/latency","Model developers optimizing reasoning capability"],"limitations":["Definition of 'reasoning-specialized' not documented — unclear which models qualify for reasoning leaderboard","Reasoning capability not separately scored — reasoning models ranked on same domains as general-purpose models","No analysis of reasoning-specialization tradeoffs (e.g., improved math but degraded general knowledge)","Reasoning evaluation methodology not detailed — unclear if evaluation specifically tests chain-of-thought or multi-step reasoning"],"requires":["Identification of reasoning-specialized models (manual curation or automated detection)","Evaluation scores from multi-domain assessment","Separate leaderboard file (reasonmodel.md) for reasoning-specialist rankings"],"input_types":["Model identifiers for reasoning-specialized models","Evaluation scores across all 8 domains","Model metadata (reasoning approach, inference time, cost)"],"output_types":["Reasoning-specialized model leaderboard (reasonmodel.md)","Separate rankings for reasoning-specialist category","Reasoning capability comparison data","Reasoning-specialization tradeoff analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Access to ReLE evaluation framework (open-source, available on GitHub)","Model API access or local deployment capability for models being evaluated","Chinese language proficiency to interpret domain-specific test questions and results","Access to leaderboard markdown files in repository","Model metadata including pricing, parameter count, and provider information","Evaluation scores from multi-domain assessment capability","Model metadata collection and curation process (manual or automated)","Leaderboard markdown files containing model information","Version control system (Git) for tracking metadata changes","Access to defect library database or export (format/API not specified in documentation)"],"failure_modes":["Evaluation methodology and scoring rubrics not fully transparent in public documentation — difficult to reproduce exact scores","Domain coverage limited to 8 major areas; specialized domains (robotics, chemistry, biology) not separately evaluated","Evaluation frequency and update cadence not specified — leaderboard staleness risk for rapidly evolving models","No per-sample error analysis or failure mode categorization within domains — only aggregate scores provided","Leaderboard tiers are static categories — no dynamic tier assignment based on real-time pricing changes","Price tier definitions not explicitly documented — unclear how models are assigned to cost brackets","Parameter size data may be outdated for frequently-updated models","No leaderboard filtering by inference speed, latency, or throughput — only ranking by evaluation scores","Metadata update frequency not specified — pricing and availability data may become stale","No structured metadata format documented — metadata embedded in markdown leaderboards rather than structured database","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3226427128495479,"quality":0.57,"ecosystem":0.52,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:57:11.504Z","last_commit":"2026-05-01T08:06:19Z"},"community":{"stars":5962,"forks":241,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jeinlee1991--chinese-llm-benchmark","compare_url":"https://unfragile.ai/compare?artifact=jeinlee1991--chinese-llm-benchmark"}},"signature":"K5fSGTq3VKAL5R5k1pyTsjSGQiSvNHjUGl8rSHxj4X+5Atul83hkQEE6N641+4eHP48y+MpzahJWlHUXQWrpCg==","signedAt":"2026-06-20T17:49:59.326Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jeinlee1991--chinese-llm-benchmark","artifact":"https://unfragile.ai/jeinlee1991--chinese-llm-benchmark","verify":"https://unfragile.ai/api/v1/verify?slug=jeinlee1991--chinese-llm-benchmark","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"}}