25 AI coding models tested on 100 real Python bugs: leaderboard
Benchmarks built on synthetic tasks tell you almost nothing about how an AI coding assistant handles real work. This one took a different approach.
The author behind this piece deliberately introduced 100 bugs into their own Python codebase, then ran 25 AI coding models against the full set. The result is a ranked leaderboard published on UX Planet.
Why This Format Matters
Most AI coding comparisons either rely on standard datasets (HumanEval, MBPP) or cherry-picked demos. Testing against bugs drawn from an actual working codebase is closer to the conditions you actually code in: messy, context-dependent, and occasionally ambiguous about what “correct” even means.
The author ran 25 models, which is a wide enough field to surface real separations between tiers rather than marginal differences at the top.
What to Do with This
The full leaderboard and methodology are behind the link. If you’re deciding which AI coding assistant to wire into your editor or CI pipeline, this is worth a read before you commit to a subscription or API budget. The headline number, 100 bugs across 25 models, is a larger sample than most individual developers would run on their own.
Read the full leaderboard on UX Planet.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
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