Reasoning · MiniMax

MiniMax-M2 on GPQA Diamond: score, rank and what it means

MiniMax-M2 scores 75.4 on GPQA Diamond, placing it #137 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
75.4
0–100 scale
Rank
#137
of 600
Percentile
77th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
11.6
points

Score distribution

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

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, MiniMax-M2 sits at #137 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 11.6 points. The median model in this index scores 68.8, which puts MiniMax-M2 above the midpoint.

MiniMax models average 65.1 on this benchmark. MiniMax-M2 is 10.3 points above that provider line, so it is a stronger result than the average MiniMax model.

Among multimodal models, the average is 73.5. MiniMax-M2 is 1.9 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 75.4 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 GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Doubao 1.5 Pro Turbo ByteDance 128000 $13.81 79.4
Grok 4 Mini xAI 65536 $12.21 77.9
Hunyuan-Large Chat Tencent 200000 $4.35 78.1
Hunyuan-Large Instruct Tencent 65536 $4.39 78.2
MiniMax-M2 MiniMax 200000 $5.19 80.6
Codestral Mistral AI 65536 $4.19 77.6
GLM-4.5 Instruct Zhipu AI 200000 $4.70 78.8

Other benchmarks for MiniMax-M2

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.8 127 79th
SWE-bench Verified 69.9 112 81th
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 GPQA Diamond?

MiniMax-M2 scores 75.4 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does MiniMax-M2 rank on GPQA Diamond?

It ranks #137 of 600 models, in the 77th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is MiniMax-M2 good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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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