Vendors love quoting benchmark scores. Here's what the major benchmarks actually measure — and how to read the numbers like a skeptic before you pick an AI coding agent.
A benchmark is a standardized test: a set of real-world-ish tasks, a fixed way to run them, and an automatic score. For AI coding agents, the well-known ones are built from actual software engineering work — GitHub issues, bug reports, feature requests — rather than toy puzzles.
But a single number hides a lot: which tasks were included, how much the agent was allowed to try, whether a human was in the loop, and which model version ran. Two vendors quoting the "same" benchmark can still be measuring different things. Treat scores as directional, not gospel.
The classic: real GitHub issues from popular Python repositories, with the actual test suites that resolved them. The agent must produce a patch that passes. SWE-bench Verified is a human-validated subset that filters out ambiguous tasks.
Tasks executed entirely in a Linux terminal: install packages, debug scripts, configure systems. It tests whether an agent can actually operate a machine, not just write code snippets.
Extensions cover more languages (SWE-bench Multilingual), front-end work judged by screenshots, and repository-scale tasks. Each answers a different question about what "good at coding" means.
Community leaderboards re-run agents on fresh tasks to fight contamination (models memorizing old benchmarks). A score on a live board is worth more than a screenshot in a launch post.
Even honest scores miss things that matter day-to-day: how the agent behaves on your codebase, how much supervision it needs, how it handles ambiguous requirements, latency, cost per task, and whether its diffs match your team's style. Benchmarks measure capability ceilings; your workflow determines the floor.
Scores below are agent-level results reported by independent leaderboards in 2026. SWE-bench Verified numbers are published per model, not per tool, so we don't republish them as tool scores.
| Agent | Terminal-Bench 2.1 | Terminal-Bench 4.0 |
|---|---|---|
| Claude Code | 89.1% (TB 2.1 agent-level, Artificial Analysis harness, Aug 2026) | 64.8% (TB 4.0, with Opus 5.5 at max effort, Oct 2026) |
| OpenAI Codex | 89.5% (TB 2.1 agent-level, Artificial Analysis harness, Aug 2026) | 58.2% (TB 4.0, with GPT-6 Astra at max effort, Oct 2026) |
| Google Antigravity | 70.7% (TB 2.1 agent-level, listed as 'Gemini CLI / Antigravity', Artificial Analysis harness, Aug 2026) | — |
Shortlist 2–3 agents, then test each on one real task. Start with the comparison.
Compare AI coding agents →Last updated: October 2026