MiniMax-M2 7B: pricing, context window and benchmarks
MiniMax-M2 7B is a small open-weight model from MiniMax. It offers a 8,192-token context window, costs $1.44 per million input tokens and $7.00 per million output tokens, and averages 60.8 across the index's benchmark suite.
Pricing
MiniMax-M2 7B costs $1.44 per million input tokens and $7.00 per million output tokens. A blended 3:1 input-to-output mix works out to about $2.83 per million tokens. Cached input is listed at $0.14 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $1.44 | per 1M tokens |
| Output | $7.00 | per 1M tokens |
| Cached input | $0.14 | per 1M tokens |
| Blended 3:1 | $2.83 | estimate per 1M tokens |
| 1M in + 1M out | $8.44 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 64.1 | 469 | 22th | |
| GPQA Diamond | 56.2 | 447 | 26th | |
| SWE-bench Verified | 45.5 | 460 | 23th | |
| HumanEval | 66.8 | 423 | 30th | |
| MATH-500 | 60.3 | 458 | 24th | |
| AIME 2025 | 54.0 | 424 | 29th | |
| LiveCodeBench | 50.2 | 447 | 26th | |
| IFEval | 72.1 | 429 | 29th | |
| TruthfulQA | 66.1 | 443 | 26th | |
| HellaSwag | 74.5 | 450 | 25th | |
| MMMU | 58.9 | 442 | 26th | |
| Arena Elo | 1280.9 | 444 | 26th |
What MiniMax-M2 7B is good for
MiniMax-M2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.44 per million input tokens it can handle a large share of production traffic.
Strengths
- Input price of $1.44 per million tokens is at or below the index median.
- Open weights make self-hosting, fine-tuning and data-residency review possible.
Trade-offs
- Context window of 8,192 tokens is small for long-document work.
- Average benchmark score is below the index median; test it on your own task.
- Less reliable on open-ended reasoning and complex multi-step instructions.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Llama 4 Scout 8B | Meta AI | 8192 | $1.46 | 61.2 |
| Mistral Large 3 7B | Mistral AI | 32768 | $1.44 | 61.1 |
| Reka Flash Nano | Reka AI | 16384 | $3.93 | 61.0 |
| MiniMax-M2 7B | MiniMax | 8192 | $1.44 | 60.8 |
| Pythia Instruct | EleutherAI | 8192 | $1.41 | 60.8 |
| SmolLM3 Base | Hugging Face | 16384 | $1.38 | 60.4 |
FAQ
How much does MiniMax-M2 7B cost?
MiniMax-M2 7B is listed at $1.44 per million input tokens and $7.00 per million output tokens. Cached input is $0.14 per million tokens. A blended 3:1 input-to-output mix is about $2.83 per million tokens.
What is the context window of MiniMax-M2 7B?
MiniMax-M2 7B has a context window of 8,192 tokens and can emit up to 8,192 output tokens per request in this index.
Is MiniMax-M2 7B open weights?
Yes. MiniMax-M2 7B is listed as an open-weight model under a open weights licence.
How does MiniMax-M2 7B score on benchmarks?
MiniMax-M2 7B averages 60.8 across the scored benchmarks in this index, ranking #445 of 600 models. Its strongest relative result is Arena Elo at 1280.9.
What is MiniMax-M2 7B best for?
MiniMax-M2 7B is a small model. That makes it a good fit for classification, extraction, routing, summarization and other high-volume jobs where latency and cost dominate. It is not the right pick for open-ended frontier reasoning, but at $1.44 per million input tokens it can handle a large share of production traffic.