MiniMax-M2 1.5B on HellaSwag: score, rank and what it means
MiniMax-M2 1.5B scores 72.0 on HellaSwag, placing it #500 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, 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-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, MiniMax-M2 1.5B sits at #500 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 24.1 points. The median model in this index scores 83.6, which puts MiniMax-M2 1.5B below the midpoint.
MiniMax models average 81.3 on this benchmark. MiniMax-M2 1.5B is 9.3 points below that provider line, so it is a weaker result than the average MiniMax model.
Among small open-weight models, the average is 70.2. MiniMax-M2 1.5B is 1.8 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 72.0 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 HellaSwag. 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 |
|---|---|---|---|---|
| DeepSeek-V3.2 1.5B | DeepSeek | 4096 | $1.02 | 56.9 |
| Llama 3 DeepInfra 8B | DeepInfra | 8192 | $1.05 | 57.4 |
| Llama 3 Modal 7B | Modal | 4096 | $0.88 | 55.2 |
| Llama 3.1 Turbo 7B | Together AI | 8192 | $0.98 | 56.4 |
| MiniMax-M2 1.5B | MiniMax | 16384 | $0.84 | 54.6 |
| Kimi K2 1.5B | Moonshot AI | 4096 | $0.99 | 56.8 |
| Mistral Large 3 1.5B | Mistral AI | 32768 | $0.82 | 55.4 |
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 | |
| LiveCodeBench | 41.5 | 530 | 12th | |
| IFEval | 65.9 | 521 | 13th | |
| TruthfulQA | 63.0 | 492 | 18th | |
| MMMU | 54.6 | 501 | 17th | |
| Arena Elo | 1238.4 | 516 | 14th |
FAQ
What score does MiniMax-M2 1.5B get on HellaSwag?
MiniMax-M2 1.5B scores 72.0 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does MiniMax-M2 1.5B rank on HellaSwag?
It ranks #500 of 600 models, in the 17th percentile. The leader is GPT-5.2 Max at 96.1.
Is MiniMax-M2 1.5B good at HellaSwag?
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 HellaSwag score calculated?
HellaSwag is a commonsense 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.