Which AI benchmarks actually matter for coding agents?
SWE-bench for resolving real repository issues and Terminal-Bench for command-line work are the closest proxies for agentic coding. Both saturate as models train toward them, so read the trend rather than the leaderboard position.
Surveyed 10 August 2026
Find AI benchmarks
Open in Tools →- 01Terminal-Bench
Terminal-Bench is a benchmark suite for evaluating how well AI agents can perform real-world tasks in terminal environments, from building a Linux kernel to configuring git servers and cracking archive hashes. It provides standardized, harbor-native challenges and a public leaderboard to quantify agent terminal mastery.
AI Agents - 02LiveBench
Frequently updated, objectively scored model benchmark spanning reasoning, coding, agentic coding, math, data analysis, language, and instruction following.
Developer Tools - 03SkillsBench
The first benchmark framework for evaluating AI agent skills across 84 diverse tasks and 7 models. It measures how well AI agents perform when equipped with domain-specific skills versus without them, providing structured evaluation across multiple abstraction layers.
AI Agents - 04Anthropic Evals
Public evaluation suite from Anthropic. Reference tasks and frameworks for benchmarking Claude and other models.
AI Tools - 05LiveCodeBench
Contamination-aware coding benchmark that continuously collects new contest problems and measures code generation, repair, and execution.
Developer Tools - 06OpenAI Evals
OpenAI's framework for evaluating LLMs and LLM systems, with an open-source registry of benchmarks the community can extend.
Developer Tools
Ordered by the VibeLeaderboard index, re-surveyed each edition. Something missing?
What to look for
- 01Does the benchmark test the task you care about? Multiple-choice reasoning tells you nothing about multi-file refactoring.
- 02Is contamination plausible? Public benchmarks leak into training data and scores drift upward without capability doing the same.
- 03Is it scored by execution or by a model? Execution-scored results are the ones you can trust.
Common questions
- What is SWE-bench?
- A benchmark built from real GitHub issues and their merged fixes. The model gets the repository and the issue, and its patch is scored by whether the project's own tests pass.
- Why does the top benchmark model not feel best in practice?
- Benchmarks measure isolated task completion under ideal context. Daily work is about following your conventions, handling ambiguity, and staying useful over a long session — none of which is scored.
More in Understand the AI landscape
- Evaluate an LLM applicationBuild test sets, score outputs, and catch quality regressions.
- Observe an LLM applicationTrace calls, inspect failures, and monitor latency, quality, and spend.
- Run models locallyUse local inference runtimes and model managers on your own hardware.
- Add vector searchStore embeddings and retrieve relevant context for AI applications.