Who it is forAn engineering lead choosing which AI coding model and CLI to pay for when the team's bug backlog keeps growing.
The problemVendor claims about coding ability are hard to compare, and a model that scores well on one task can cost far more than a close rival.
The outcomeA shortlist of models with their Fixed /105 score, per-repo scores, and cost, so the choice rests on measured bug fixes.
What you need first
- A web browser to open the live leaderboard at bughunt.productcompass.pm
- Access to the results/ folder on GitHub or a local copy of the repo for the raw data
Steps
- Open the live board at bughunt.productcompass.pm.
- Read the Fixed /105 column and the Repo 1 /45 and Repo 2 /60 columns to see where each model does well.
- Compare the Cost column for the models that score within a few points of the top.
- Open the Method page at bughunt.productcompass.pm/method and read the definitions and caveats before you decide.
Prompt for your agent
Using the bug-hunt-bench leaderboard at bughunt.productcompass.pm, list the top five models by Fixed /105 along with their Repo 1 and Repo 2 scores and Cost for <task or budget note>. Output a short table. Verify that each score matches the row in the README table before reporting it.
Caveat. Scores count only the 105 planted bugs in this bench, so a high score says little about bugs outside it.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for phuryn/bug-hunt-bench and "Choose a coding model for bug fixing by score and cost"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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