MiniMax-M2 1.5B on MATH-500: score, rank and what it means
MiniMax-M2 1.5B scores 53.5 on MATH-500, placing it #530 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.
Score distribution
The chart shows the top 10 models on MATH-500, with MiniMax-M2 1.5B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 3 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 4 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
| 5 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | Gemini 3 Pro Reasoning | Google DeepMind | 200000 | $19.69 | 87.5 |
| 10 | Claude Opus 4.5 Turbo | Anthropic | 2000000 | $19.57 | 85.7 |
What this score means
On MATH-500, MiniMax-M2 1.5B sits at #530 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 43.2 points. The median model in this index scores 76.0, which puts MiniMax-M2 1.5B below the midpoint.
MiniMax models average 71.1 on this benchmark. MiniMax-M2 1.5B is 17.6 points below that provider line, so it is a weaker result than the average MiniMax model.
Among small open-weight models, the average is 53.7. MiniMax-M2 1.5B is 0.2 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 53.5 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 MATH-500. 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 |
|---|---|---|---|---|
| Qwen3 1.5B | Alibaba Cloud | 4096 | $0.84 | 53.5 |
| MiniMax-Text 1.5B | MiniMax | 32768 | $0.58 | 51.5 |
| Mixtral Groq 8B | Groq | 32768 | $0.77 | 54.8 |
| Yi-Large 8B | 01.AI | 32768 | $0.88 | 56.1 |
| MiniMax-M2 1.5B | MiniMax | 16384 | $0.84 | 54.6 |
| GLM-4.5 1.5B | Zhipu AI | 16384 | $0.65 | 53.3 |
| Kimi K1.5 1.5B | Moonshot AI | 4096 | $0.75 | 53.2 |
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 | |
| AIME 2025 | 39.6 | 543 | 10th | |
| 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 MATH-500?
MiniMax-M2 1.5B scores 53.5 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.
How does MiniMax-M2 1.5B rank on MATH-500?
It ranks #530 of 600 models, in the 12th percentile. The leader is GPT-5.2 Max at 96.7.
Is MiniMax-M2 1.5B good at MATH-500?
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 MATH-500 score calculated?
MATH-500 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.