Mathematics · MiniMax

MiniMax-M2 1.5B on AIME 2025: score, rank and what it means

MiniMax-M2 1.5B scores 39.6 on AIME 2025, placing it #543 of 600 models in the index. The benchmark average is 63.6 and the leader is Claude Opus 4.5 Pro at 91.4.

Score
39.6
0–100 scale
Rank
#543
of 600
Percentile
10th
within the index
Benchmark avg
63.6
all models
Leader
91.4
Claude Opus 4.5 Pro
Gap to leader
51.8
points

Score distribution

The chart shows the top 10 models on AIME 2025, with MiniMax-M2 1.5B highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 91.4
GPT-5.2 Pro 91.0
GPT-5.1 Pro 90.6
GPT-5.2 90.6
o4 Reasoning 90.3
o4 Pro 89.9
Claude Opus 4.5 89.8
Gemini 3 Pro Max 89.6
Claude Opus 4.5 Max 89.3
Claude Opus 4.5 Reasoning 88.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 GPT-5.2 OpenAI 2000000 $19.71 88.0
5 o4 Reasoning OpenAI 1000000 $19.69 87.5
6 o4 Pro OpenAI 1000000 $19.37 87.3
7 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
8 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
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 AIME 2025, MiniMax-M2 1.5B sits at #543 of 600. The leader, Claude Opus 4.5 Pro, scores 91.4, so the gap to the top is 51.8 points. The median model in this index scores 67.6, which puts MiniMax-M2 1.5B below the midpoint.

MiniMax models average 63.7 on this benchmark. MiniMax-M2 1.5B is 24.1 points below that provider line, so it is a weaker result than the average MiniMax model.

Among small open-weight models, the average is 41.6. MiniMax-M2 1.5B is 2.0 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 39.6 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 $0.84 per million input tokens, MiniMax-M2 1.5B is below the median input price of $4.00 across the 600 models scored on AIME 2025. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
DBRX 7B Databricks 8192 $0.65 51.9
Llama 3.3 1.5B Meta AI 4096 $0.61 52.2
QwQ 1.5B Alibaba Cloud 32768 $0.61 51.6
Arctic 8B Snowflake 8192 $0.65 52.4
MiniMax-M2 1.5B MiniMax 16384 $0.84 54.6
GLM-4.5 1.5B Zhipu AI 16384 $0.65 53.3
Magistral 1.5B Mistral AI 4096 $0.67 52.7

Other benchmarks for MiniMax-M2 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 59.6 531 12th
GPQA Diamond 49.9 525 13th
SWE-bench Verified 42.8 492 18th
HumanEval 58.3 520 13th
MATH-500 53.5 530 12th
LiveCodeBench 41.5 530 12th
IFEval 65.9 521 13th
TruthfulQA 63.0 492 18th
HellaSwag 72.0 500 17th
MMMU 54.6 501 17th
Arena Elo 1238.4 516 14th

FAQ

What score does MiniMax-M2 1.5B get on AIME 2025?

MiniMax-M2 1.5B scores 39.6 on AIME 2025 in this index. The benchmark average is 63.6 and the top score is 91.4.

How does MiniMax-M2 1.5B rank on AIME 2025?

It ranks #543 of 600 models, in the 10th percentile. The leader is Claude Opus 4.5 Pro at 91.4.

Is MiniMax-M2 1.5B good at AIME 2025?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.

How is the AIME 2025 score calculated?

AIME 2025 is a mathematics 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 1.5B cost?

MiniMax-M2 1.5B costs $0.84 per million input tokens and $2.96 per million output tokens.

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