agents benchmark
SWE Atlas - Test Writing evaluates a model's ability to author meaningful tests for real-world software projects.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score30.83% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis swe atlas - test writing AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
MiniMax M3 currently leads SWE Atlas - Test Writing with 30.83%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What SWE Atlas - Test Writing measures and how its scores work.
SWE Atlas - Test Writing is a benchmark for evaluating test writing by models or agents.
It measures how well agents understand code and produce correct, useful test coverage, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about SWE Atlas - Test Writing.
MiniMax M3 is currently ranked first with 30.83%.
The current leaders are MiniMax M3 (30.83%).
MiniMax M3 has the lowest matched official input price at $0.30 input / $1.2 output per 1M tokens.
The fastest matched records are MiniMax M3 (6.61 tok/s via MiniMax).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures how well agents understand code and produce correct, useful test coverage, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.