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| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
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.
Unique: Combines 8 specialized domain evaluations (Medical, Finance, Law, etc.) with ~300 evaluation dimensions specifically designed for Chinese LLMs, rather than generic language benchmarks. Aggregates individual question scores (1-5 scale) into normalized domain scores (0-100) then composite rankings, enabling cross-domain capability comparison. Maintains 2M+ defect library linking model failures to specific domains for root-cause analysis.
vs alternatives: Deeper domain specialization than MMLU or C-Eval (which focus on general knowledge) and Chinese-specific evaluation design vs English-centric benchmarks like HELM or LMSys Chatbot Arena
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.
Unique: Implements multi-dimensional leaderboard organization (commercial/open-source primary split, then price tier or parameter size secondary split) with separate ranked lists for reasoning-specialized models. Uses markdown-based leaderboard storage (commerce2.md, reasonmodel.md, alldata.md) enabling version control and community contributions. Maintains model metadata (provider, parameters, pricing) alongside evaluation scores for context-aware comparison.
vs alternatives: More granular category-based filtering than MMLU leaderboards (which use single global ranking) and explicit price-tier organization vs Hugging Face Model Hub (which lacks domain-specific performance context)
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.
Unique: Maintains comprehensive metadata for 298+ models (name, version, provider, parameters, pricing, availability) alongside evaluation scores in leaderboard files. Enables attribute-based filtering and comparison (by provider, parameter size, pricing tier). Tracks model versions and evolution over time within version-controlled repository.
vs alternatives: Integrated metadata with evaluation scores vs separate model registries (Hugging Face, OpenRouter) and version-controlled metadata history vs static model information
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.
Unique: Aggregates 2M+ model failures into indexed defect library linked to specific evaluation questions, domains, and models — enabling systematic error pattern analysis rather than just aggregate scores. Supports cross-model error comparison to identify shared weaknesses and domain-specific failure distributions. Provides raw failure examples for fine-tuning and adversarial testing rather than only summary statistics.
vs alternatives: More comprehensive failure documentation than MMLU or C-Eval (which report only aggregate accuracy) and enables error-driven model improvement vs score-only benchmarks
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.
Unique: Incorporates Gaokao (Chinese college entrance exam) level questions into evaluation framework, testing academic-level Chinese language understanding and writing quality. Combines general language proficiency assessment with domain-specific language tasks (medical terminology, legal documents, financial reports in Chinese). Uses 1-5 quality scale for response evaluation rather than binary correctness, capturing nuanced language performance.
vs alternatives: Chinese-specific academic assessment vs English-centric benchmarks (MMLU, HELM) and Gaokao-level difficulty calibration vs generic language benchmarks
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.
Unique: Evaluates mathematical reasoning with 1-5 quality scale for reasoning steps rather than binary correctness, enabling partial credit for correct methodology with computational errors. Combines final answer accuracy with reasoning quality assessment to capture mathematical thinking capability. Includes multi-step reasoning problems and logical inference tasks beyond simple arithmetic.
vs alternatives: More nuanced mathematical assessment than MMLU (binary correctness) and captures reasoning quality vs answer-only evaluation
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.
Unique: Evaluates four professional domains (Medical, Finance, Law, Administrative) using domain-expert-designed test questions with realistic scenarios (medical case studies, financial analysis, legal document interpretation) rather than generic knowledge questions. Incorporates domain-specific scoring rubrics reflecting professional standards and best practices. Enables cross-domain comparison to identify models suitable for professional applications.
vs alternatives: More specialized domain assessment than general benchmarks (MMLU, C-Eval) and realistic professional scenarios vs academic knowledge questions
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.
Unique: Specialized evaluation of psychological health knowledge and mental health counseling capability using domain-specific test questions. Incorporates 1-5 quality scale for psychological reasoning assessment. Addresses sensitive domain requiring both knowledge accuracy and ethical appropriateness in responses.
vs alternatives: Dedicated mental health domain assessment vs general benchmarks lacking psychological expertise, and explicit safety consideration for sensitive mental health applications
+3 more capabilities
Executes SQL queries against Supabase PostgreSQL instances through the Model Context Protocol, translating natural language or structured query requests into parameterized SQL statements. Uses MCP's tool-calling interface to expose database operations as callable functions with schema validation, enabling LLM agents to perform CRUD operations, joins, and aggregations with automatic connection pooling and credential management through Supabase client SDK.
Unique: Exposes Supabase PostgreSQL as MCP tools with automatic credential injection from Supabase client SDK, eliminating manual connection string management and enabling seamless LLM-to-database queries within Claude or compatible agents
vs alternatives: Tighter integration than generic SQL MCP servers because it leverages Supabase's built-in authentication and connection pooling rather than requiring separate database credential configuration
Exposes Supabase Auth session state and user metadata through MCP tools, allowing agents to inspect current authentication context, retrieve user profiles, and trigger auth-related operations. Integrates with Supabase's JWT-based auth system to validate sessions and access user claims without re-authenticating, using the Supabase client's built-in session management.
Unique: Integrates Supabase's JWT-based auth system directly into MCP tool interface, allowing agents to inspect and act on auth state without managing separate credential stores or re-authentication flows
vs alternatives: More seamless than generic auth MCP servers because it leverages Supabase's built-in session management and avoids redundant credential passing between agent and auth system
Invokes Supabase Edge Functions (serverless TypeScript/JavaScript functions) through MCP tools, passing parameters and receiving results with optional streaming support. Uses Supabase's edge function HTTP API to trigger functions with automatic authentication headers and response parsing, enabling agents to execute custom business logic without embedding it in the agent itself.
chinese-llm-benchmark scores higher at 44/100 vs Supabase at 42/100.
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Unique: Exposes Supabase Edge Functions as MCP tools with automatic authentication and response parsing, allowing agents to invoke custom serverless logic without managing HTTP clients or credential injection
vs alternatives: More integrated than generic HTTP MCP tools because it handles Supabase-specific authentication, error handling, and response formatting automatically
Subscribes to real-time changes on Supabase tables through MCP's event streaming interface, using Supabase's PostgreSQL LISTEN/NOTIFY mechanism to push INSERT, UPDATE, and DELETE events to agents. Maintains persistent WebSocket connections and filters events by table and row-level policies, enabling agents to react to database changes without polling.
Unique: Bridges Supabase's PostgreSQL LISTEN/NOTIFY real-time system with MCP's tool interface, enabling agents to subscribe to database changes without managing WebSocket connections or event serialization
vs alternatives: More efficient than polling-based approaches because it uses Supabase's native real-time infrastructure rather than repeated database queries
Manages files in Supabase Storage buckets through MCP tools, supporting upload, download, list, and delete operations with automatic authentication and path-based access control. Uses Supabase's S3-compatible storage API with built-in support for public/private buckets and signed URLs for temporary access, enabling agents to handle file I/O without managing cloud storage credentials.
Unique: Exposes Supabase Storage's S3-compatible API as MCP tools with automatic authentication and signed URL generation, eliminating the need for agents to manage cloud storage credentials or generate temporary access tokens
vs alternatives: More integrated than generic S3 MCP tools because it leverages Supabase's built-in bucket policies and authentication rather than requiring separate AWS credentials
Performs semantic similarity searches on vector embeddings stored in Supabase PostgreSQL using pgvector extension, translating natural language queries into embedding vectors and executing cosine/L2 distance searches. Integrates with embedding providers (OpenAI, Cohere) or uses pre-computed embeddings, enabling agents to retrieve semantically similar documents or records without full-text search limitations.
Unique: Integrates pgvector directly into MCP tools with automatic embedding generation and distance calculation, enabling agents to perform semantic search without managing separate vector database infrastructure
vs alternatives: More efficient than external vector databases (Pinecone, Weaviate) for Supabase users because it colocates embeddings with relational data, reducing network latency and simplifying data synchronization
Exposes Supabase database schema information through MCP tools, allowing agents to discover table structures, column types, constraints, and relationships without manual schema documentation. Queries PostgreSQL information_schema and Supabase metadata tables to dynamically generate schema descriptions, enabling agents to construct valid queries and understand data relationships.
Unique: Queries Supabase's PostgreSQL information_schema directly through MCP tools, enabling agents to dynamically discover and adapt to database schemas without pre-configured schema definitions
vs alternatives: More flexible than static schema definitions because it reflects live database state, including recent migrations or schema changes
Enforces Supabase Row-Level Security policies within agent queries, ensuring that agents can only access rows permitted by RLS rules defined in the database. Evaluates policies based on authenticated user context (JWT claims, user ID) and applies WHERE clause filters automatically, preventing unauthorized data access at the database layer rather than application layer.
Unique: Delegates authorization enforcement to PostgreSQL RLS policies rather than implementing authorization in agent code, ensuring that data access rules are centralized and cannot be bypassed by agent logic
vs alternatives: More secure than application-level authorization because RLS is enforced at the database layer, preventing accidental data leaks even if agent code has bugs
+1 more capabilities