SWE Atlas - Codebase QnA
SWE Atlas - Codebase QnA evaluates a model's ability to answer questions about real codebases, measuring repository-level comprehension and the ability to reason about code structure, behavior, and intent across an entire project.
MiniMax M3 from MiniMax currently leads the SWE Atlas - Codebase QnA leaderboard with a score of 0.379 across 1 evaluated AI models.
What SWE Atlas - Codebase QnA measures
SWE Atlas - Codebase QnA is a text benchmark that evaluates large language models on agents and code tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.4, with the leader reaching 0.4.
Compare leaders on the best AI for agents and best AI for code leaderboards.
MiniMax M3 leads with 37.9%.
Progress Over Time
Interactive timeline showing model performance evolution on SWE Atlas - Codebase QnA
SWE Atlas - Codebase QnA Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  MiniMax M3New MiniMax | — | 1.0M | $0.60 / $2.40 |
FAQ
Common questions about SWE Atlas - Codebase QnA.
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