Code agent benchmarks in 2026: why SWE-Bench Pro brings expectations back to earth
An honest take on SWE-Bench Pro, LiveCodeBench, and why contaminated benchmarks overestimate models. Template to evaluate agents on your own tasks.
- Published
- April 19, 2026
- min read
- 7 min read
- Categoría
- Engineering
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3 chaptersChapter 01
The gap between Verified and Pro tells the story
SWE-Bench Verified is the marketing-friendly benchmark: curated tasks, reproducible, easy to show. Top models clear 70%. SWE-Bench Pro takes 1,865 multi-file, multi-language tasks (~107 lines, 4.1 files on average). The same models drop to 23%.
The takeaway isn't that the models are bad. It's that anyone promising “full autonomy” is talking about the easy benchmark. Your codebase looks more like the hard one.
- OpenAI GPT-5 and Claude Opus 4.1 score 23.3% and 23.1% respectively on SWE-Bench Pro.
- Verified is plateauing; Pro leaves clear room to improve.
- Multi-file + multi-language + human-judged acceptance = closer to your actual work.
Chapter 02
Why LiveCodeBench also matters
Traditional benchmarks suffer contamination: tasks end up inside the next generation's training set. LiveCodeBench refreshes problems continuously, so scores don't get inflated by memorization.
If you only look at one benchmark, watch that one plus SWE-Bench Pro. Enough to avoid buying hype.
Chapter 03
How to evaluate agents on your own repo
Public benchmarks give you the floor. Your internal eval gives you the ceiling. The most useful pattern we see with clients: build a fixed set of 30 to 60 real tasks and run them against each model every quarter.
- Mix tasks: bug fix, bounded refactor, small feature, writing tests, docs.
- Define success up front: tests green + human review in under N minutes.
- Measure cost per task (tokens + time) and review time, not only success rate.
- Re-run for each new model; drop the ones that don't beat the current one by a clear margin.
Written by
Wasyra Engineering
Modernization, architecture, and reliable delivery
Wasyra Engineering documents patterns for moving legacy systems without freezing delivery or breaking ownership.
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