Coding · MiniMax

MiniMax-M2 on SWE-bench Verified: score, rank and what it means

MiniMax-M2 scores 69.9 on SWE-bench Verified, placing it #112 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
69.9
0–100 scale
Rank
#112
of 600
Percentile
81th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
11.8
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with MiniMax-M2 highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
3 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
4 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 o4 Max OpenAI 2000000 $19.51 86.2
8 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On SWE-bench Verified, MiniMax-M2 sits at #112 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 11.8 points. The median model in this index scores 61.0, which puts MiniMax-M2 above the midpoint.

MiniMax models average 57.0 on this benchmark. MiniMax-M2 is 12.9 points above that provider line, so it is a stronger result than the average MiniMax model.

Among multimodal models, the average is 66.4. MiniMax-M2 is 3.5 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 69.9 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $5.19 per million input tokens, MiniMax-M2 is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 4 Scout Instruct Meta AI 32768 $4.59 79.1
Nova Pro Reasoning Amazon 200000 $13.48 79.4
Kimi K2 Chat Moonshot AI 400000 $5.74 82.1
Kimi K1.5 Chat Moonshot AI 128000 $5.11 80.2
MiniMax-M2 MiniMax 200000 $5.19 80.6
MiniMax-M2 Instruct MiniMax 128000 $5.11 79.4
ERNIE 4.5 Reasoning Baidu 1000000 $14.36 79.8

Other benchmarks for MiniMax-M2

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.8 127 79th
GPQA Diamond 75.4 137 77th
HumanEval 88.0 73 88th
MATH-500 82.3 157 74th
AIME 2025 78.1 115 81th
LiveCodeBench 73.4 121 80th
IFEval 86.1 153 75th
TruthfulQA 83.5 93 85th
HellaSwag 90.4 76 87th
MMMU 78.0 98 84th
Arena Elo 1423.8 130 78th

FAQ

What score does MiniMax-M2 get on SWE-bench Verified?

MiniMax-M2 scores 69.9 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does MiniMax-M2 rank on SWE-bench Verified?

It ranks #112 of 600 models, in the 81st percentile. The leader is GPT-5.2 Pro at 81.7.

Is MiniMax-M2 good at SWE-bench Verified?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the SWE-bench Verified score calculated?

SWE-bench Verified is a coding benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does MiniMax-M2 cost?

MiniMax-M2 costs $5.19 per million input tokens and $22.73 per million output tokens.

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