Pick the right eval for your agent type, budget, and risk tolerance. Compare 16+ benchmarks across coding, reasoning, web, desktop, multi-agent, and domain tasks.
Recommended benchmark
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Select your agent focus, effort, and budget to see a recommendation.
Real-world GitHub issues from popular Python repositories. The agent must edit code so the repository's own test suite passes.
Pros: Highly realistic; directly measures production software engineering ability.
Cons: Expensive to run; requires sandboxed execution and repo setup; not all issues are agent-friendly.
Best fit: Code-generation agents, AI software engineers, and autonomous PR bots.
164 hand-written Python function-completion problems with unit-test validation.
Pros: Cheap, fast, and widely cited; great for comparing pass@1 coding skill across models.
Cons: Short functions only; may be memorized by large models; does not test repo-level work.
Best fit: Quick model comparisons for code completion and small-function generation.
Crowd-sourced Python programming problems with test cases, from beginner to intermediate difficulty.
Pros: Larger and more varied than HumanEval; includes docstrings and natural-language prompts.
Cons: Still focused on small, self-contained functions; less realistic than repo-level benchmarks.
Best fit: Entry-level coding assistants and educational coding agents.
Multi-step question answering that requires reasoning, web browsing, file parsing, and tool use to answer real-world questions.
Pros: Very hard to game; rewards agents that can plan, search, and synthesize evidence.
Cons: Slow and expensive; requires web access; answers can be brittle to search-result changes.
Best fit: General-purpose assistant agents, research agents, and agents with tool access.
57 multiple-choice tasks covering STEM, humanities, social sciences, and professional knowledge.
Pros: Standardized, cheap, and widely reported; good baseline for world knowledge.
Cons: Static multiple-choice benchmark; does not measure multi-step reasoning or agentic behavior.
Best fit: Foundation-model selection and knowledge-heavy assistant agents.
Continuously updated reasoning tasks drawn from recent data sources, designed to avoid contamination.
Pros: Harder to memorize; stays current; covers math, reasoning, language, and instruction following.
Cons: Less established than MMLU or HumanEval; questions can be noisy.
Best fit: Agents that must stay current or claim strong general reasoning.
Independent, realistic web tasks across e-commerce, social forums, software development, maps, and admin panels.
Pros: Simulates real websites; measures end-to-end task completion in a live environment.
Cons: Requires hosted environments; can be flaky due to website changes; expensive to run at scale.
Best fit: Browser agents, web assistants, and autonomous research agents.
Visual web navigation tasks where the agent must interact with live websites through screenshots and predicted actions.
Pros: Tests vision + web interaction; closer to how humans browse visually.
Cons: Latency is high; requires screenshot rendering; brittle to UI redesigns.
Best fit: Vision-enabled browser agents and multimodal assistants.
Diverse web tasks on real websites across multiple domains, annotated with expert demonstrations.
Pros: Large coverage of web domains; includes both action prediction and task completion metrics.
Cons: Benchmark is static; newer website versions may diverge from the dataset.
Best fit: Generalist web agents and UI automation tools.
Open-ended tasks inside a real Ubuntu desktop: file management, browser use, office apps, and system configuration.
Pros: Real OS environment; captures messy, long-horizon computer use.
Cons: Very expensive and slow; needs VM infrastructure; high variance across runs.
Best fit: Desktop automation agents, personal assistants, and OS-control agents.
Tasks on a live Windows desktop spanning settings, apps, and cross-application workflows.
Pros: Tests the most common consumer OS; surfaces enterprise automation value.
Cons: Windows-specific licensing and VM setup; evaluation pipeline is complex.
Best fit: Enterprise desktop-automation agents and Windows-centric copilots.
Eight environments (OS, database, knowledge graph, digital card game, web shopping, household, etc.) that exercise decision making, tool use, and multi-turn interaction.
Pros: Broad multi-environment coverage; compares agents across many dimensions.
Cons: Some environments are synthetic; setup can be heavy; scoring is nuanced.
Best fit: Generalist agents, tool-use agents, and multi-environment systems.
Multi-agent collaboration tasks such as negotiation, team problem solving, and resource allocation.
Pros: Targets the unique failure modes of multi-agent communication and coordination.
Cons: Smaller community; less standardized than single-agent benchmarks.
Best fit: Multi-agent orchestrators, agent teams, and collaborative AI systems.
Grade-school math word problems that require multi-step arithmetic reasoning.
Pros: Clean, cheap, and widely used; strong signal for chain-of-thought reasoning.
Cons: Narrow domain; not representative of real agent deployments.
Best fit: Tutoring agents, math copilots, and reasoning-heavy assistants.
Capture-the-flag and cybersecurity challenges ranging from reconnaissance to exploit development.
Pros: Directly measures offensive-security and vulnerability-research capability.
Cons: High stakes; requires isolated sandbox; not suitable for public leaderboards without review.
Best fit: Security research agents, red-team tools, and vulnerability-assessment copilots.
Open-ended science experiments in a text-based simulation where agents must design and execute experiments.
Pros: Tests scientific reasoning and long-horizon planning in a controlled simulator.
Cons: Niche; requires domain expertise to interpret results; simulator limitations affect generalization.
Best fit: Scientific research assistants, lab-automation agents, and hypothesis-testing systems.
No benchmarks match those filters. Try widening your effort or budget.
Last updated: 2026-07-28. See notes.
Start with HumanEval or MBPP for a quick, cheap signal on function-level coding. Move to SWE-bench once you need repo-level, real-world software engineering validation.
GAIA is currently the most respected general-purpose benchmark because it requires multi-step reasoning, tool use, and web search. It is expensive to run, so pair it with cheaper MMLU or LiveBench checks for faster iteration.
Use WebArena, Mind2Web, or WebVoyager for browser agents. For desktop/OS agents, use OSWorld (Ubuntu) or Windows Agent Arena. These require sandboxed environments and are the most expensive to run.
MMLU measures static multiple-choice knowledge, not agentic behavior. It is a useful model-selection filter but tells you almost nothing about tool use, planning, or long-horizon task completion.
Cheap benchmarks like HumanEval, MBPP, MMLU, and GSM8K cost under $100 per run. Realistic benchmarks like SWE-bench, GAIA, and WebArena range from $300–$5,000 per run. OS-level evals like OSWorld can exceed $1,000–$10,000 per run depending on sample size and VM infrastructure.
Benchmark costs and effort levels are directional estimates based on public leaderboards and API pricing. Replace with your own vendor quotes for exact budgeting.
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