Coding · MiniMax

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

MiniMax-M2 scores 73.4 on LiveCodeBench, placing it #121 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
73.4
0–100 scale
Rank
#121
of 600
Percentile
80th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
11.7
points

Score distribution

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

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, MiniMax-M2 sits at #121 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 11.7 points. The median model in this index scores 64.7, which puts MiniMax-M2 above the midpoint.

MiniMax models average 60.0 on this benchmark. MiniMax-M2 is 13.4 points above that provider line, so it is a stronger result than the average MiniMax model.

Among multimodal models, the average is 70.3. MiniMax-M2 is 3.1 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 73.4 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 LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
o3 Vision OpenAI 1000000 $15.24 81.8
Magistral Instruct Mistral AI 65536 $4.48 78.4
Doubao 1.5 Pro Max ByteDance 400000 $14.93 81.2
Nova Pro Amazon 32768 $12.75 79.4
MiniMax-M2 MiniMax 200000 $5.19 80.6
Gemini 3 Flash Vision Google DeepMind 400000 $15.06 81.9
MiniMax-Text Chat MiniMax 128000 $4.26 77.8

Other benchmarks for MiniMax-M2

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.8 127 79th
GPQA Diamond 75.4 137 77th
SWE-bench Verified 69.9 112 81th
HumanEval 88.0 73 88th
MATH-500 82.3 157 74th
AIME 2025 78.1 115 81th
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 LiveCodeBench?

MiniMax-M2 scores 73.4 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does MiniMax-M2 rank on LiveCodeBench?

It ranks #121 of 600 models, in the 80th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is MiniMax-M2 good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench 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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