| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 10 decomposed | 9 decomposed |
| Times Matched | 0 | 0 |
Evaluates and ranks 496+ AI models across three independent dimensions (intelligence, speed, cost) using a proprietary Intelligence Index v4.0 that synthesizes 10 named benchmarks (GDPval-AA, τ²-Bench Telecom, Terminal-Bench Hard, SciCode, AA-LCR, AA-Omniscience, IFBench, Humanity's Last Exam, GPQA Diamond, CritPt) into a single numerical score. The platform aggregates these metrics into a sortable, filterable leaderboard that updates as new model versions and providers enter the market, enabling side-by-side comparison of model capabilities without requiring users to run their own evaluations.
Unique: Combines 10 distinct benchmark suites into a single proprietary Intelligence Index rather than relying on single-benchmark rankings like MMLU or HumanEval alone, providing a more holistic capability assessment across reasoning, coding, and domain knowledge. The platform continuously tracks 496+ models including open-source variants, not just major commercial APIs.
vs alternatives: More comprehensive than individual benchmark leaderboards (MMLU, ARC, HumanEval) because it synthesizes multiple evaluation dimensions; more current than academic papers because it updates monthly; more objective than vendor marketing because it's independent and aggregates third-party benchmarks.
Implements a personalized model recommendation system that accepts user-defined weights for intelligence, speed, and cost, then applies algorithmic filtering to surface optimal models matching those priorities. The engine appears to use rule-based or weighted-scoring logic to rank models by the user's stated trade-off preferences, enabling teams to quickly identify models that fit their specific operational constraints (e.g., 'fastest models under $1/1M tokens' or 'highest intelligence within 50ms latency budget').
Unique: Treats model selection as a multi-objective optimization problem where users can dynamically weight intelligence, speed, and cost rather than forcing a single ranking. This approach acknowledges that different teams have different constraints and priorities, unlike static leaderboards that rank all models by a single metric.
vs alternatives: More flexible than provider comparison tools (which show only one vendor's models) because it spans all providers; more practical than academic benchmarks because it includes pricing and latency alongside capability; more transparent than vendor-provided recommendations because it's independent.
Newly launched AA-AgentPerf capability that benchmarks AI agents on real agent workloads using actual hardware setups, moving beyond model-only evaluation to measure end-to-end agent performance including tool calling, planning, and execution overhead. This capability captures how agents perform on practical tasks (not just raw model capability) and accounts for infrastructure factors like latency, memory, and concurrent request handling that affect production deployments.
Unique: Measures agents on real workloads with real hardware rather than synthetic benchmarks, capturing end-to-end performance including tool calling, planning, and framework overhead. This is distinct from model-only benchmarks because it accounts for the full agent stack, not just the underlying LLM.
vs alternatives: More practical than model-only benchmarks because it measures what users actually deploy; more realistic than framework vendor benchmarks because it's independent and compares across frameworks; more comprehensive than latency-only metrics because it includes success rate and throughput.
Provides domain-specific benchmark indices (Coding Index, Agentic Index, and reasoning capability indicators) that isolate model performance on specialized tasks beyond general intelligence. The platform marks models with reasoning capabilities (indicated by lightbulb icon) and maintains separate leaderboards for coding-specific evaluation, allowing users to find models optimized for their specific task domain rather than relying on general-purpose rankings.
Unique: Separates model evaluation by task domain (coding, reasoning, agentic) rather than treating all models as general-purpose, recognizing that a model's strength in one domain doesn't guarantee strength in another. The reasoning capability indicator provides a quick filter for models suitable for complex reasoning tasks.
vs alternatives: More targeted than general leaderboards because it isolates performance on specific task types; more practical for specialists than one-size-fits-all rankings; more discoverable than searching individual benchmark papers because indices are pre-computed and filterable.
Evaluates and compares AI agent platforms and frameworks (not just models) across capabilities, pricing, and supported integrations. The platform provides agent-specific comparison tables that help users choose between different agentic systems (e.g., comparing agents built on Claude vs GPT-4 vs open-source, or comparing agent orchestration platforms), including filtering by use case (general work, coding, customer support) and platform features.
Unique: Treats agents as first-class comparison objects (not just models) and evaluates them on platform-specific dimensions like integrations, pricing models, and use-case suitability rather than just underlying model capability. This acknowledges that agent selection involves both model choice and platform/framework choice.
vs alternatives: More comprehensive than individual agent vendor websites because it compares across platforms; more practical than model-only rankings because it includes platform features and pricing; more discoverable than searching agent documentation because comparisons are pre-built and filterable.
Maintains a timestamped changelog of model ranking changes, new model additions, and benchmark updates, allowing users to track how the model landscape has evolved over time. The changelog shows dated entries (e.g., April 20-24, 2024) indicating when models were added, re-evaluated, or changed position in rankings, providing transparency into platform updates and enabling users to understand which changes are due to new models vs re-evaluation of existing models.
Unique: Provides explicit transparency into when and how rankings change, rather than silently updating leaderboards. This allows users to distinguish between ranking changes due to model re-evaluation vs new models entering the market vs benchmark methodology changes.
vs alternatives: More transparent than model vendor websites (which don't publish ranking changes); more detailed than social media announcements (which miss many updates); more structured than blog posts (which are harder to search and filter).
Publishes original analysis articles and commentary on model releases, capability trends, and competitive dynamics (e.g., 'DeepSeek is back among the leading open weights models'). These editorial pieces provide context and interpretation beyond raw benchmark numbers, helping users understand the significance of ranking changes and emerging trends in the model landscape. Content is authored by the Artificial Analysis team and appears alongside benchmark data to provide narrative context.
Unique: Combines benchmark data with original editorial analysis rather than presenting raw numbers alone, providing narrative context that helps users interpret what ranking changes mean for their decisions. This positions Artificial Analysis as an analyst platform, not just a data aggregator.
vs alternatives: More authoritative than social media commentary because it's backed by benchmark data; more timely than academic papers; more focused than general AI news because it concentrates on model capability and market dynamics.
Provides a responsive web dashboard where users can select models, adjust comparison criteria, and view side-by-side metrics in real-time. The interface supports filtering by use case, reasoning capability, and custom metric weighting, with interactive tables and charts that update as users modify their selections. The dashboard is designed for quick exploration and decision-making without requiring API calls or command-line tools.
Unique: Focuses on interactive exploration and visual comparison rather than static leaderboards, allowing users to dynamically adjust criteria and see results update in real-time. The interface is designed for decision-making workflows, not just data browsing.
vs alternatives: More user-friendly than API-based tools because it requires no technical setup; more flexible than static leaderboards because users can customize comparisons; more discoverable than spreadsheets because filtering and sorting are built-in.
+2 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.
Supabase scores higher at 42/100 vs Artificial Analysis at 24/100. Artificial Analysis leads on quality, while Supabase is stronger on ecosystem. 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