SWE-Perf

Software Engineering Performance benchmark measuring code optimization capabilities

MiniMax M2.1 from MiniMax currently leads the SWE-Perf leaderboard with a score of 0.031 across 1 evaluated AI models.

About this benchmark

What SWE-Perf measures

SWE-Perf is a text benchmark that evaluates large language models on code tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.0, with the leader reaching 0.0.

Compare leaders on the best AI for code leaderboards.

MiniMaxMiniMax M2.1 leads with 3.1%.

Progress Over Time

Interactive timeline showing model performance evolution on SWE-Perf

State-of-the-art frontier
Open
Proprietary

SWE-Perf Leaderboard

1 models
ContextCostLicense
1230B1.0M$0.30 / $1.20
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FAQ

Common questions about SWE-Perf.

What is the SWE-Perf benchmark?

Software Engineering Performance benchmark measuring code optimization capabilities

What is the SWE-Perf leaderboard?

The SWE-Perf leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, MiniMax M2.1 by MiniMax leads with a score of 0.031. The average score across all models is 0.031.

What is the highest SWE-Perf score?

The highest SWE-Perf score is 0.031, achieved by MiniMax M2.1 from MiniMax.

How many models are evaluated on SWE-Perf?

1 models have been evaluated on the SWE-Perf benchmark, with 0 verified results and 1 self-reported results.

What categories does SWE-Perf cover?

SWE-Perf is categorized under code. The benchmark evaluates text models.

What is the best open-source model on SWE-Perf?

MiniMax M2.1 by MiniMax is the top-ranked open-source model on SWE-Perf, with a score of 0.031 (rank #1).

Which model offers the best value on SWE-Perf?

Among models scoring within 10% of the leader, MiniMax M2.1 from MiniMax is the cheapest, at $0.30 per million input tokens with a score of 0.031.

How recent are the SWE-Perf leaderboard results?

The SWE-Perf leaderboard was last updated in June 2026 and currently includes 1 evaluated models.

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