| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 6 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Collects human preference judgments by presenting users with side-by-side model outputs for identical prompts, recording which response is preferred. Uses a tournament-style ranking system where pairwise comparison results are aggregated into Elo ratings, enabling continuous benchmarking without fixed test sets. The leaderboard updates dynamically as new human votes accumulate, with statistical confidence intervals computed from vote counts.
Unique: Uses continuous crowdsourced pairwise comparisons with Elo rating aggregation rather than static benchmark datasets, allowing real-time ranking updates as community votes accumulate. Enables evaluation on arbitrary user-submitted prompts instead of fixed test sets, capturing performance on diverse real-world use cases.
vs alternatives: More representative of practical model performance than fixed benchmarks (MMLU, HumanEval) because it captures preference on diverse user-submitted tasks, and more scalable than hiring professional evaluators since it leverages community voting.
Manages parallel inference calls to multiple LLM endpoints (OpenAI, Anthropic, open-source models via HuggingFace) for the same prompt, with response caching to avoid redundant API calls for identical inputs. Implements request batching and timeout handling to ensure responsive UI even when some model endpoints are slow or unavailable. Responses are cached by prompt hash, reducing API costs and latency for repeated evaluations.
Unique: Implements response caching at the prompt level across multiple model providers, reducing redundant API calls while maintaining fair comparison conditions. Uses parallel inference with timeout-based fallbacks to ensure responsive evaluation even when some endpoints are degraded.
vs alternatives: More cost-efficient than naive multi-model comparison because response caching eliminates duplicate API calls, and more reliable than sequential inference because parallel calls with timeout handling prevent slow models from blocking the UI.
Computes Elo ratings from pairwise vote data and displays rankings with confidence intervals derived from vote counts and win/loss ratios. Uses Bayesian posterior estimation to quantify uncertainty in rankings, showing which models are statistically significantly different versus within margin of error. Leaderboard updates incrementally as new votes arrive, with ranking stability metrics to indicate when a model's position is reliable.
Unique: Combines Elo rating aggregation with Bayesian confidence interval estimation to quantify ranking uncertainty, making statistical reliability explicit rather than hidden. Enables incremental leaderboard updates as votes accumulate while maintaining confidence bounds that reflect data sparsity.
vs alternatives: More statistically rigorous than simple win-rate rankings because confidence intervals account for vote count, and more transparent than fixed-benchmark leaderboards because uncertainty is quantified and displayed.
Organizes user-submitted prompts into predefined categories (writing, coding, reasoning, etc.) and tracks model performance separately per category. Enables stratified analysis showing which models excel at specific task types versus overall. Category-level statistics reveal performance gaps (e.g., model A dominates writing but underperforms on reasoning) that aggregate rankings would obscure.
Unique: Stratifies leaderboard rankings by prompt category, revealing domain-specific model strengths that aggregate rankings obscure. Enables users to find best-fit models for specific applications rather than relying on single overall score.
vs alternatives: More actionable than single-score leaderboards because it shows which models excel at specific tasks, and more representative than category-agnostic benchmarks because it captures real-world use case diversity.
Provides a web-based interface (built with Gradio or Streamlit on HuggingFace Spaces) for users to submit prompts, view side-by-side model responses, and vote on preferences. Implements real-time leaderboard updates visible to all users, with sorting/filtering by model name, rating, category, or region. Voting interface includes response metadata (latency, token count) to inform user decisions.
Unique: Integrates voting interface, response display, and live leaderboard in a single Gradio/Streamlit app, lowering friction for community participation. Displays response metadata (latency, tokens) alongside rankings to inform voting decisions.
vs alternatives: More accessible than command-line or API-based evaluation because it requires no technical setup, and more transparent than closed leaderboards because users see voting counts and methodology.
Tracks leaderboard rankings across geographic regions and time periods, enabling users to filter results by location (US, EU, Asia) and date range. Stores vote timestamps and regional metadata, allowing analysis of how model preferences vary by region or how rankings evolve over time. Temporal filtering reveals model improvement trajectories and seasonal trends in evaluation patterns.
Unique: Enables stratified leaderboard analysis across both geographic regions and time periods, revealing how model preferences vary by location and how rankings evolve. Stores temporal metadata to support historical trend analysis.
vs alternatives: More insightful than static leaderboards because temporal filtering reveals model improvement trajectories, and more globally representative because regional filtering exposes preference variations.
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.
Supabase scores higher at 42/100 vs arena-leaderboard at 21/100. arena-leaderboard leads on ecosystem, while Supabase is stronger on quality.
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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
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