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| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 7 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Aggregates standardized benchmark results (MMLU, HumanEval, GSM8K, etc.) across dozens of LLM providers and open-source models, normalizing scores to a common scale and enabling side-by-side performance comparison. Uses a centralized data pipeline that ingests results from official model cards, academic papers, and third-party evaluation frameworks, then surfaces them through a unified comparison interface with filtering and sorting by benchmark category.
Unique: Centralizes fragmented benchmark data from heterogeneous sources (official model cards, academic papers, leaderboards) into a single normalized schema, enabling direct comparison across models that may not have been evaluated on identical benchmark suites
vs alternatives: More comprehensive than individual model cards and faster than manually cross-referencing papers; differs from Hugging Face Open LLM Leaderboard by including commercial models and pricing data alongside benchmarks
Maintains a real-time or frequently-updated database of input/output token pricing for LLM APIs (OpenAI, Anthropic, Google, etc.) and calculates effective cost per token, cost per 1M tokens, and total inference cost for a given token volume. Implements a pricing normalization layer that handles variable pricing tiers (e.g., GPT-4 Turbo vs GPT-4o), batch discounts, and context window-dependent pricing, allowing users to estimate total cost of ownership for a workload.
Unique: Implements a multi-dimensional pricing model that normalizes across different pricing structures (per-token, per-request, context-window-dependent) and automatically recalculates when providers update rates, rather than static pricing tables
vs alternatives: More current than manual spreadsheets and includes more models than individual provider pricing pages; differs from LLM cost calculators by integrating pricing with performance benchmarks for cost-per-quality analysis
Maintains a structured database of model specifications including context window size, maximum output tokens, requests-per-minute limits, tokens-per-minute throughput, and latency characteristics. Allows filtering and comparison of models by these constraints, enabling builders to identify models that fit specific architectural requirements (e.g., 'models with 200K+ context window and <100ms latency').
Unique: Consolidates scattered specification data from multiple provider documentation pages into a single queryable schema with consistent units and filtering, enabling constraint-based model selection rather than manual documentation review
vs alternatives: Faster than reading individual model cards and enables filtering by multiple constraints simultaneously; differs from provider dashboards by aggregating across all providers in one place
Provides a structured matrix comparing discrete capabilities across models: vision support, function calling, JSON mode, streaming, fine-tuning availability, multimodal input types, and other feature flags. Implements a capability taxonomy that normalizes heterogeneous feature naming across providers (e.g., 'tool use' vs 'function calling') and surfaces which models support which features with version/tier specificity.
Unique: Normalizes capability naming across providers (OpenAI, Anthropic, Google, etc.) into a unified taxonomy and tracks version-specific feature availability, rather than treating each provider's feature set as isolated
vs alternatives: More comprehensive than individual provider feature pages and enables cross-provider capability discovery; differs from model cards by explicitly highlighting which models lack specific features
Maintains a chronological database of model releases, updates, and deprecations with dates and version information. Tracks which models are in active development, maintenance, or deprecated status, and surfaces upcoming model releases or sunset dates. Enables filtering by release date range and status to identify stable vs. cutting-edge models.
Unique: Aggregates release and deprecation information from multiple provider announcements and documentation into a unified timeline view with forward-looking alerts, rather than requiring manual monitoring of each provider's blog
vs alternatives: Proactive deprecation warnings vs. reactive discovery when a model is removed; differs from provider release notes by cross-referencing all providers in one timeline
Tracks benchmark scores over time for models as they are updated or new versions are released, enabling visualization of performance trends and comparison of how models have improved or degraded. Implements time-series data storage and visualization to show performance trajectories across benchmark categories, allowing users to assess whether a model is improving or stagnating.
Unique: Maintains time-series benchmark data with version tracking, enabling trend visualization and velocity analysis rather than just point-in-time snapshots; requires continuous data collection and normalization across benchmark versions
vs alternatives: Reveals performance trajectories that static comparisons miss; differs from individual model release notes by aggregating trends across all models and benchmarks in one view
Implements a multi-dimensional filtering engine that allows simultaneous filtering across pricing, performance, context window, capabilities, and other dimensions, with optional constraint optimization to find the 'best' model according to user-defined weights. Uses a scoring algorithm that combines multiple metrics (cost, performance, latency, context window) into a composite ranking, enabling users to express complex requirements like 'cheapest model with >90% MMLU score and 100K context window'.
Unique: Combines multiple filtering dimensions with optional multi-objective optimization, allowing users to express complex requirements as a single query rather than iteratively filtering across separate pages
vs alternatives: More flexible than single-dimension sorting and faster than manual comparison; differs from provider comparison tools by supporting cross-provider filtering with weighted optimization
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 LLM Stats at 19/100. Supabase also has a free tier, making it more accessible.
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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