Send an issue and a repo URL. Get back a validated diff. Kodah resolves real production bugs at 55% accuracy on SWE-bench — for four cents per fix. No GPT-4. No Claude Opus. No tricks.
Send an issue description and a repo URL. Kodah processes it in an isolated container and returns a production-ready patch. That's the entire interface.
No IDE plugins. No codebase uploads. No configuration files. Your code is cloned, processed, and destroyed — nothing persists.
The industry assumes you need the biggest, most expensive model to fix real bugs. Kodah proves otherwise — resolving 55% of SWE-bench issues using only efficient, cost-optimized models.
The secret isn't a better model. It's a better way to understand code. When you give an AI the right context — structured, precise, and complete — a lightweight model outperforms a flagship running blind.
280 out of 300 issues produced valid patches. No retries. No prompt chains. One shot.
280 of 300 issues produced a valid patch on the first attempt
Patches that pass the original test suite — real fixes, not guesses
Context building + agent reasoning combined. Not a typo.
The entire SWE-bench Lite evaluation cost less than a lunch
Other tools throw tokens at the problem with a $3 flagship model and hope for the best. Kodah delivers better accuracy at a fraction of the cost because the approach is fundamentally different.
Instead of dumping raw files into a prompt, Kodah gives the AI a structured, precise understanding of your codebase. Better input beats bigger model — every time.
93.3% patch generation on the first attempt. No retry loops. No multi-pass chains. No "let me try again." Send an issue, get a fix.
The system runs your existing test suite against its own patch before returning it. You get fixes that pass tests, not suggestions that "might work."
Your code is cloned into an ephemeral container, processed in memory, and destroyed after each job. Nothing is stored. Nothing persists. Nothing leaves.
$0.04 per issue — while competitors burn $0.50–$1.00 on flagship API calls for worse results. The entire 300-issue benchmark cost $13.59.
One curl command in your GitHub Action. Auto-triage new issues. Auto-generate fix PRs. The only bottleneck left is human review.
SWE-bench Lite: 300 real GitHub issues from production Python repos — Django, Scikit-learn, Matplotlib, Sympy, and more. No synthetic tests. No cherry-picked demos.
| System | Resolve Rate | Model | Cost / Issue |
|---|---|---|---|
| RAG + GPT-4 | ~18% | GPT-4 | $0.50+ |
| SWE-Agent | ~23% | GPT-4 | $1.00+ |
| Aider | ~26% | GPT-4 / Opus | $0.30+ |
| AutoCodeRover | ~30% | GPT-4 | $0.65 |
| Kodah | 55% | Efficient | $0.04 |
Competitor scores from the SWE-bench public leaderboard. Kodah evaluated on all 300 SWE-bench Lite issues.
"Efficient" = non-flagship model. Exact architecture undisclosed.
Kodah doesn't just fix more bugs. It costs 10–25x less per fix than systems using flagship models — and still resolves nearly twice as many issues.
Average cost per issue across the full SWE-bench Lite benchmark.
Kodah's total cost for all 300 issues: $13.59.
No subscriptions. No seat licenses. Pay only for resolved issues.
Get your API key in 30 seconds. First 10 fixes are on us.