Coding · MiniMax

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

MiniMax-M2 Chat scores 70.4 on LiveCodeBench, placing it #184 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
70.4
0–100 scale
Rank
#184
of 600
Percentile
69th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
14.7
points

Score distribution

The chart shows the top 10 models on LiveCodeBench, with MiniMax-M2 Chat 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 Chat sits at #184 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 14.7 points. The median model in this index scores 64.7, which puts MiniMax-M2 Chat above the midpoint.

MiniMax models average 60.0 on this benchmark. MiniMax-M2 Chat is 10.4 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 Chat is 0.1 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 70.4 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 $5.02 per million input tokens, MiniMax-M2 Chat 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
Hunyuan-Turbo Reasoning Tencent 128000 $11.89 77.5
Llama 3 Lambda Instruct Lambda 32768 $3.92 75.8
Reka Core Max Reka AI 128000 $13.41 79.3
Llama 3.3 Groq Groq 32768 $3.98 76.8
MiniMax-M2 Chat MiniMax 200000 $5.02 80.2
Llama 3 Replicate Chat Replicate 200000 $3.74 74.7
Yi-Lightning Instruct 01.AI 128000 $4.16 76.5

Other benchmarks for MiniMax-M2 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 84.3 75 88th
GPQA Diamond 77.8 91 85th
SWE-bench Verified 66.4 182 70th
HumanEval 84.2 128 79th
MATH-500 86.0 93 85th
AIME 2025 78.3 109 82th
IFEval 88.0 102 83th
TruthfulQA 80.3 172 71th
HellaSwag 87.8 151 75th
MMMU 79.2 70 88th
Arena Elo 1443.0 93 85th

FAQ

What score does MiniMax-M2 Chat get on LiveCodeBench?

MiniMax-M2 Chat scores 70.4 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does MiniMax-M2 Chat rank on LiveCodeBench?

It ranks #184 of 600 models, in the 69th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is MiniMax-M2 Chat 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 Chat cost?

MiniMax-M2 Chat costs $5.02 per million input tokens and $18.95 per million output tokens.

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