2026 AI Coding Agent Benchmark: Cursor, Claude Code, and Codex – Who Leads?

Evaluation framework

SWE‑Bench‑Pro‑Hard‑AA – real‑world bug‑fix simulation.

Terminal‑Bench v2 – assessment of terminal tool‑chain usage.

SWE‑Atlas‑QnA – evaluation of code‑base understanding.

Composite ranking

Cursor CLI + Opus 4.7 leads with 61 points . Codex (GPT‑5.5) and Claude Code (Opus 4.7) each achieve 60 points , so the top three differ by only one point.

Upgrading Claude Code from Sonnet 4.6 to Opus 4.7 raises its score from 49 to 60 (+11 points).

Token consumption

Most tokens are spent on cache hits. Claude Code + GLM‑5.1 consumes 4.8 M tokens per task, about three times the 1.5 M tokens used by Cursor CLI + Opus 4.7 . When scores are comparable, selecting a cheaper model can cut costs by an order of magnitude.

Cost per task

Cheapest configuration: Cursor CLI + Composer 2 at $0.07 per task.

Most expensive configuration: Claude Code + GLM‑5.1 at $2.26 per task (≈32 × difference).

Claude Code + DeepSeek V4 Pro costs $0.35 per task and scores 50 points , roughly one‑third the cost of Opus 4.7 with only ~17 % performance loss.

Execution time

Fastest setup: Claude Code + Opus 4.7 (Anthropic direct) completes a task in 5.8 minutes with a 60‑point score.

Claude Code + Kimi K2.6 requires 41.5 minutes for the same 50‑point score, indicating a longer inference chain and higher API latency. High‑frequency iteration scenarios must factor in waiting costs.

Key observations

Domestic models DeepSeek V4 Pro , Kimi K2.6 , and GLM‑5.1 appear on the leaderboard; they are competitive but often incur higher token usage and longer runtimes.

DeepSeek V4 Pro offers the best cost‑performance trade‑off: $0.35 per task, 50‑point index, cost ≈⅓ of Opus 4.7 with modest performance drop.

Future differentiation is expected to shift from raw performance scores to cost efficiency, token consumption, and latency.

Code example

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权转自 菜鸟教程（ID：runoob）
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RUNOOB