# MiniMax-M2 sets an open-weights high on a leading index, with caveats

- **Published:** 2025-10-29
- **Updated:** 2026-10-12
- **Category:** Models & Benchmarks
- **Author:** Sinthetix
- **URL:** https://sinthetix.com/blog/minimax-m2-open-weights-coding-model

> MiniMax's M2 is a sparse open-weights model aimed at coding and agents. What the independent analysis shows and what to check before using it.

This week the open-weights field has a new reference point. Artificial Analysis, an independent benchmarking service, reported that MiniMax's M2 "achieves a new all-time-high Intelligence Index score for an open weights model" [1]. For businesses weighing self-hosted against hosted AI, the details matter more than the headline.

**Key takeaways**

- M2 is a very sparse model: roughly 10B parameters are active per token out of a total of 230B per the model card (200B per Artificial Analysis) [1][2].
- Artificial Analysis reports it as the highest-scoring open-weights model on its Intelligence Index [1].
- It is verbose, which offsets part of its low per-token price [1].
- Check the license text and run your own tests before committing.

## What was released

MiniMax published the weights of M2, a mixture-of-experts model built for coding and agent workflows, on or about October 27, 2025: the model card says "Today, we release and open source MiniMax-M2" without a date, and Artificial Analysis's analysis is dated October 27 [1][2]. Artificial Analysis lists 200B total parameters with 10B active per forward pass; MiniMax's own model card says 230B total with 10B active (its file metadata shows about 229B) [1][2]. We take the vendor's 230B as the more likely figure, but the two sources disagree, so verify before sizing hardware. In a mixture-of-experts design only a fraction of the model runs for each token, which keeps serving cost closer to that of a small model.

The model card shows the Artificial Analysis Intelligence Index at 61 and 69.4 on SWE-bench Verified, a software-engineering benchmark [2]. The card's license field reads "modified-mit" while the page body links to an MIT license, so read the license text itself [2].

## Price and efficiency

Through MiniMax's own API, M2 costs $0.30 per million input tokens and $1.20 per million output tokens [1]. Anthropic's Claude Sonnet 4.5, released a month earlier, is priced at $3 and $15 [3]. The model is verbose, however: it used about 120 million tokens to complete Artificial Analysis's index evaluations, tied for the highest of the models it compared [1]. Cost per task, not per token, is the number to test.

|  | MiniMax-M2 | Claude Sonnet 4.5 |
| --- | --- | --- |
| Weights available | Yes | No |
| Input / output price per million tokens | $0.30 / $1.20 [1] | $3 / $15 [3] |
| SWE-bench Verified | 69.4% (vendor-reported) [2] | 77.2% (vendor-reported) [3] |

The SWE-bench figures come from each vendor under its own test setup and are not strictly comparable. The Sonnet 4.5 number is Anthropic's primary score averaged over 10 trials [3].

## Caveats

Artificial Analysis noted that models such as DeepSeek V3.2 and Qwen3 235B may still outperform M2 at some generalist tasks [1]. A strong index score is not a guarantee on your workload, and the license, data-handling and security review of any model you host remain your responsibility.

## Practical implication

Open-weights models are now credible candidates for coding and agent tasks where data must stay in-house or per-token cost dominates. Test one against your own tasks, measuring total tokens used and not just the price list.

## Sources

1. [MiniMax M2 Benchmarks and Analysis](https://www.artificialanalysis.ai/articles/minimax-m2-benchmarks-and-analysis), Artificial Analysis, 2025-10-27
2. [MiniMax-M2 model card](https://huggingface.co/MiniMaxAI/MiniMax-M2), MiniMax (Hugging Face), 2025-10-27
3. [Introducing Claude Sonnet 4.5](https://www.anthropic.com/news/claude-sonnet-4-5), Anthropic, 2025-09-29

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