AI Benchmarks

General AI Assistants benchmark: real-world questions requiring tool use, multi-step reasoning, and web/file interaction — hard for both humans and agents.

Realistic web environment benchmark where agents must complete tasks on self-hosted sites (e.g. e-commerce, CMS, forums) via browser actions.

Challenging Chatbot Arena prompts packaged for automatic evaluation — a harder preference-style benchmark derived from real user battles.

Evaluates systems on real GitHub issues: given a repository and issue, produce a patch that passes the project’s tests.

Classic function-level Python code generation benchmark: models complete function bodies from docstrings and signatures.

Contamination-free LLM benchmark with frequently updated questions across math, coding, reasoning, language, instruction following, and data analysis.

Holistic Evaluation of Language Models (Stanford CRFM): multi-metric, multi-scenario evaluation emphasizing transparency and taxonomy of use cases.

Massive Multitask Language Understanding evaluates broad knowledge and problem-solving across 57 subjects spanning STEM, humanities, and social sciences.

Bilingual long-context benchmark covering single/multi-document QA, summarization, few-shot learning, synthetic tasks, and code across long inputs.

Massive Multi-discipline Multimodal Understanding: college-level problems combining images and text across six disciplines.

Benchmark suite for retrieval-augmented generation systems — evaluating retrieval quality, groundedness, and answer correctness on realistic RAG workloads.

Comprehensive RAG benchmark from Meta stressing retrieval failures, outdated knowledge, and answer faithfulness across realistic industry scenarios.

Graduate-Level Google-Proof Q&A: PhD-level science questions designed so that non-experts with web search still struggle, testing deep domain reasoning.