AI news · Research · Co-Design Bench
Best coding agent fully finishes only 26.4% of real user requests
A benchmark built from 26,314 real coding sessions finds the best of ten models meets 69.9% of a task’s criteria on average but rarely all of them.
AI coding agents do most of a job and leave something undone. Co-Design Bench, a benchmark built by researchers from Alibaba and several Asian universities, gives agents 273 change requests taken from real users’ coding sessions, The Decoder reported on Sunday 8 November 2026. The best of ten models, GPT-6 Astra, meets 69.9% of a task’s criteria on average but fully completes only 26.4% of tasks, the paper says.
The tasks come from 37,463 requests across 26,314 sessions, picked where users had to rephrase a request or roll back a change. A small job might mean reading a figure off a receipt and fixing every total built on it, and a big one means reworking a whole project. Grading uses two AI judges: one works the app in a browser, the other checks each pass for mock-ups or answers typed in rather than worked out.
Claude Opus 5 ranks second and Qwen3.8-Max third, while Grok 4.6 finishes tenth of ten. Every model does worse when a task mixes three or four kinds of change, such as layout, design, interaction and content. GLM-5.2 used nearly six times GPT-6 Astra’s tokens (the text units models bill by) on small changes and still scored lower.
Our price sheet, checked 8 November, shows Claude Pro at $20 a month with Claude Code, Anthropic’s coding agent, included.
For a marketer who builds landing pages with an agent, the 26.4% figure means a human check on every change. The agent usually gets most of a revision right and misses at least one detail, such as a total that no longer adds up.
Sources
- The Decoder: AI coding agents still need human cleanup on nearly three out of four tasks. Checked
- arXiv: Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic Models. Checked
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