AI Benchmarks
Standardized test suites that measure AI model and system performance — from code benchmarks like HumanEval and SWE-bench to reasoning tests like MMLU and GPQA, agent evaluations like WebArena, and chat quality benchmarks like MT-Bench.
Local Deep Research achieves ~95% on SimpleQA benchmark (tested with GPT-4.1-mini). Supports local and cloud LLMs (Ollama, Google, Anthropic, ...). Searches 10+ sources - arXiv, PubMed, web, and your private documents. Everything Local & Encrypted.
Zero-shot LLM evaluation for reasoning tasks.
Benchmark for dangerous knowledge in LLMs.
Real-world user query benchmark judged by GPT-4.
Realistic web environment for autonomous agent testing.
16-dimension benchmark for video generation quality.
8-dimension trustworthiness benchmark for LLMs.
Human-verified benchmark for AI coding agents.
AI coding agent benchmark — real GitHub issues, end-to-end evaluation, the standard for code agents.
OpenAI's factuality benchmark for hallucination detection.
11K safety evaluation questions across 7 categories.
Real-world visual QA requiring spatial reasoning.
Real OS benchmark for multimodal computer agents.
Hugging Face open-source LLM leaderboard — standardized benchmarks, automatic evaluation.
Embedding model benchmark — 8 tasks, 112 languages, the standard for comparing embeddings.
Multi-turn conversation benchmark — 80 questions, 8 categories, GPT-4 as judge.
Expert-level multimodal understanding across 30 subjects.
57-subject knowledge benchmark — 15K+ questions across STEM, humanities, professional domains.
Visual mathematical reasoning benchmark.
12.5K competition math problems — AMC/AIME/Olympiad level, 7 subjects, standard math benchmark.
Crowdsourced LLM evaluation — side-by-side blind voting, Elo ratings, most trusted LLM benchmark.
Continuously updated coding benchmark — new competitive programming problems, prevents contamination.
Continuously updated contamination-free LLM benchmark.
Google's benchmark for verifiable instruction following.
Hardest exam questions from thousands of experts.
OpenAI's code generation benchmark — 164 Python problems with unit tests, pass@k evaluation.
Stanford's holistic LLM evaluation — 42 scenarios, 7 metrics including fairness, bias, toxicity.
8.5K grade school math problems — multi-step reasoning, verifiable solutions, reasoning benchmark.
Graduate-level expert QA — unsearchable questions in biology, physics, chemistry for deep reasoning.
Expert-level math problems created by mathematicians.
Crowdsourced Elo ratings from human model comparisons.
Comprehensive code benchmark — 1,140 practical tasks with real library usage beyond HumanEval.
Abstract reasoning benchmark with $1M prize for AGI.
Automatic LLM evaluation — instruction-following, LLM-as-judge, length-controlled, cost-effective.
Multi-language AI coding benchmark — tests code editing ability across 10+ languages.
8-environment benchmark for evaluating LLM agents.
leaderboard — AI demo on HuggingFace
arena-leaderboard — AI demo on HuggingFace
Harness real-time AI to spot trends, analyze videos, benchmark...
Track trends, analyze sentiment, benchmark...
UGI-Leaderboard — AI demo on HuggingFace
bigcode-models-leaderboard — AI demo on HuggingFace
Boost e-commerce with AI-driven chat, analytics, and multilingual...
Evaluation framework for RAG and LLM applications
PromptBench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts. It provides a convenient infrastructure to simulate **black-box** adversarial **prompt attacks** on the models and evaluate their performances.
OpenMMLab Detection Toolbox and Benchmark
The LLM Evaluation Framework
Expert-driven LLM benchmarks and updated AI model leaderboards.
An open platform for crowdsourced AI benchmarking, hosted by researchers at UC Berkeley SkyLab and LMArena.
Artificial Analysis provides objective benchmarks & information to help choose AI models and hosting providers.
What are AI Benchmarks?
AI benchmarks and evaluation suites measure model capabilities across specific tasks. From general reasoning (MMLU, HellaSwag) to code generation (HumanEval, SWE-bench), math (GSM8K, MATH), and safety (HarmBench). Benchmarks are critical for model selection, but interpreting them requires understanding what they actually measure and their limitations.
How to Choose
Choose benchmarks that measure what matters for YOUR use case. General benchmarks (MMLU) tell you about broad capability. Task-specific benchmarks (HumanEval for code, SWE-bench for real-world software engineering) are more predictive of actual performance. Always supplement with your own evaluation on your specific task.
Key Capabilities to Evaluate
Common Patterns
Select the correct answer from options. MMLU, ARC, HellaSwag. Easy to score, but doesn't test generation quality.
Generate code, run it, check output. HumanEval, MBPP. Tests functional correctness, not code quality.
Complete a real-world task in a sandboxed environment. SWE-bench, WebArena. Most realistic, but expensive to run.
Human judges rate outputs. Chatbot Arena. Most reliable for subjective quality, but expensive and slow.
What to Watch Out For
Top Capabilities
Browse all →Analyzes selected code or entire files and generates natural language explanations of what the code does, how it works, and why certain patterns were chosen. The feature can produce documentation in multiple formats (docstrings, comments, markdown) and supports various documentation styles (JSDoc, Sphinx, etc.). Developers can request explanations at different levels of detail (high-level overview, line-by-line breakdown, architectural context) through the chat interface, with responses appearing as formatted text or code comments.
Translates non-English speech directly to English text using the same Transformer encoder-decoder architecture by prepending a 'translate' task token during decoding, bypassing explicit transcription. The AudioEncoder processes mel spectrograms identically to transcription, but the TextDecoder generates English tokens directly from audio embeddings. This end-to-end approach avoids cascading errors from intermediate transcription-then-translation pipelines and enables language-agnostic audio understanding.
Detects the spoken language in audio by analyzing the AudioEncoder embeddings and using the TextDecoder to predict a language token before generating transcription text. Language detection is implicit in the multitask training; the model learns to identify language from acoustic features without a separate classification head. Supports 99 languages with varying confidence based on training data representation (English: 65% of training data, others: 0.1-2%).
Maintains conversation history within a single chat session, allowing developers to ask follow-up questions, request refinements, and build on previous responses without re-providing context. The extension manages conversation state (messages, responses, context) and sends the full conversation history to ChatGPT's API with each request, enabling contextual understanding of refinement requests like 'make it faster' or 'add error handling'.
Generates new code snippets based on natural language descriptions by sending the user's intent and current editor selection context to OpenAI's API, then inserting the generated code at the cursor position or displaying it in the sidebar. The extension reads the active editor's selected text to provide code context, enabling the model to generate syntactically appropriate code for the detected language. Generation is triggered via keyboard shortcut (Ctrl+Alt+G), command palette, or toolbar button.
Generates docstrings, comments, and API documentation for functions, classes, and modules by analyzing code structure and semantics using GPT-4o. The extension detects function signatures, parameter types, and return types, then generates documentation in multiple formats (JSDoc, Python docstrings, Javadoc, etc.) matching the language and project conventions. Generated docs are inserted inline with proper indentation and formatting.
Analyzes staged or modified code changes in the current Git repository and generates descriptive commit messages using the configured AI provider. The feature integrates with VS Code's Git context to identify changed files and diffs, then sends this information to the AI model to produce commit messages following conventional commit formats or project-specific conventions. This automation reduces the cognitive load of writing commit messages while maintaining code quality and repository history clarity.
Offers a freemium pricing structure where basic problem detection and explanations are available for free, with premium features (likely advanced fix generation, priority support, or higher API quotas) available through paid subscription. The free tier includes GNN-based problem detection and LLM-powered explanations using Metabob's default backend, while premium tiers likely unlock OpenAI ChatGPT integration, higher analysis quotas, or team features. Pricing details are not publicly documented in the marketplace listing.
Browse Other Types
Autonomous AI systems that act on your behalf
ModelsFoundation models, fine-tunes, and specialized AI models
MCP ServersModel Context Protocol tools and integrations
RepositoriesOpen-source AI projects on GitHub
APIsProgrammatic endpoints for AI capabilities
ExtensionsBrowser and IDE extensions powered by AI
View all 14 types →Frequently Asked Questions
Which AI benchmarks matter most?
It depends on your use case. For general reasoning: MMLU and HellaSwag. For code: HumanEval and SWE-bench. For math: GSM8K and MATH. For real-world chat quality: Chatbot Arena. Always supplement benchmarks with evaluation on your specific task — benchmarks measure general capability, not fitness for your use case.