Benchmark-Bericht
Methodik und szenarienspezifische Ergebnisse für Codna vs. führende Coding-Agenten.
Ressourcen
Guides, Benchmarks und Launch-Assets.
Erfahre, wie deterministisches Codebase-Verständnis autonome Code-Reparatur verändert.
Frequently asked
Codna was tested head-to-head against Cursor. It used 5× fewer tokens and ran 1.7× faster, with every fix test-verified.
Token consumption, wall-clock speed, and verified fix rate across 8 real bug-fix scenarios run against OpenAI Codex CLI and Google Gemini CLI. Every fix counted only when your own tests passed.
A deterministic engine maps the repository in roughly 60ms without any LLM calls. It then hands the AI agent an evidence bundle of around 600 tokens — measured 162x smaller than reading the full repo — so the agent fixes the right code immediately.
Yes — Codna supports 250+ languages, and the engine mapped 130 repositories in 9.2 seconds, consuming zero tokens for the mapping step.
Codna locates the affected code using its dependency and blast-radius graph, generates a fix from the evidence bundle, then runs your tests to verify the result. On GitHub, it opens a pull request with the verified fix.
You can self-host the engine, bring your own API key, and configure fail-closed egress. Codna never trains on your code.