MiniMax-M2 1.5B on LiveCodeBench: score, rank and what it means
MiniMax-M2 1.5B scores 41.5 on LiveCodeBench, placing it #530 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 distribution
The chart shows the top 10 models on LiveCodeBench, with MiniMax-M2 1.5B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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 1.5B sits at #530 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 43.6 points. The median model in this index scores 64.7, which puts MiniMax-M2 1.5B below the midpoint.
MiniMax models average 60.0 on this benchmark. MiniMax-M2 1.5B is 18.5 points below that provider line, so it is a weaker result than the average MiniMax model.
Among small open-weight models, the average is 41.2. MiniMax-M2 1.5B is 0.3 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 41.5 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 LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Mixtral 8B | Mistral AI | 8192 | $0.91 | 55.4 |
| Phi-4 8B | Microsoft | 32768 | $0.96 | 54.9 |
| Mixtral 7B | Mistral AI | 16384 | $0.70 | 54.1 |
| Firesearch Nano | Fireworks AI | 8192 | $2.49 | 55.6 |
| MiniMax-M2 1.5B | MiniMax | 16384 | $0.84 | 54.6 |
| Kimi K1.5 1.5B | Moonshot AI | 4096 | $0.75 | 53.2 |
| Llama 3 Anyscale 7B | Anyscale | 4096 | $0.98 | 56.2 |
Other benchmarks for MiniMax-M2 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| 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 | |
| AIME 2025 | 39.6 | 543 | 10th | |
| 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 LiveCodeBench?
MiniMax-M2 1.5B scores 41.5 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does MiniMax-M2 1.5B rank on LiveCodeBench?
It ranks #530 of 600 models, in the 12th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is MiniMax-M2 1.5B good at LiveCodeBench?
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 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 1.5B cost?
MiniMax-M2 1.5B costs $0.84 per million input tokens and $2.96 per million output tokens.