Mistral Vibe for Code vs Claude Code vs Cursor vs Codex: Four Agents Scored on One Scaffold-to-PR Task

Coding agents are the most contested category in developer tooling right now. Four names dominate the shortlist: Mistral Vibe for Code , Claude Code , Cursor , and OpenAI Codex . Each claims to take a feature from prompt to pull request.

This comparison runs all four against one practical workflow. Not a toy script. A real unit of engineering work: scaffold a feature across multiple files, generate and run tests, then open a pull request.

Prompt: ‘In our existing Python/FastAPI service, add a /subscriptions endpoint. Scaffold the route, the Pydantic models, and a service layer across the correct files. Generate unit and integration tests, run them, fix any failures, then open a pull request with a clear description.’

That prompt maps to three stages every agent must clear: scaffold, test, ship.

This is a capability comparison , not a single timed run on one machine. Scores reflect documented features, published benchmarks, and vendor specifications as of July 14, 2026. Each dimension is rated 1 to 5, for 25 points maximum. Every score carries a one-line reason.

Three caveats that matter more than the scores:

The five dimensions: feature scaffolding, test generation and run loop, PR and async workflow, surface coverage, and cost/openness/control.

Mistral Vibe is Mistral’s unified agent for work and code, and the product formerly known as Le Chat. Its coding surface runs an open-source CLI on GitHub under Apache 2.0.

The model stack is layered, and the distinction is important. Per Mistral’s own FAQ , Mistral Medium is the most performant family for complex, multi-step software engineering. The Vibe CLI and IDE plugins are powered by Devstral . Codestral handles fast completions, and Codestral Embed powers semantic code search. Remote agents run on Mistral Medium 3.5. Judging Vibe solely by Devstral’s benchmark understates the product.

On scaffolding, Vibe scans your file tree and Git status for project-aware context, then does multi-file orchestration with architecture-level reasoning. Devstral 2 is a 123B dense model with a 256K context window. Mistral reports it at 72.2% on SWE-bench Verified, which it positions as state of the art among open models. Devstral Small 2 (24B, Apache 2.0) scores 68.0% and runs on consumer hardware.

Testing is a first-class feature: auto-generated tests that evolve with the codebase, matched to your existing patterns. Hooks run custom shell commands before and after each agent turn, so you can enforce conventions or block patterns automatically.