MiniMaxReleased on Jun 16, 2025

MiniMax M1 40K: Benchmarks, Pricing & Size

MiniMax M1 40K is a language model from MiniMax, released in June 2025.

MiniMax-M1 is an open-source, large-scale reasoning model that uses a hybrid-attention architecture for efficient long-context processing. It supports up to a 1 million token context window and 80,000-token reasoning output, matching

MiniMax M1 40K model size

MiniMax M1 40K has 456 billion parameters and was trained on 7.5 trillion tokens. See how it compares to other models in the same parameter range.

ParametersTraining tokens
456B
7.5Ttokens
16× tokens-to-params ratio
Frontier (200B+)
456B
1B7B70B405B

MiniMax M1 40K API

API access coming soon

MiniMax M1 40K will be available through our gateway shortly.

MiniMax M1 40K examples

Recent arena outputs from MiniMax M1 40K, picked from the highest-ranked matchups.

MiniMax M1 40K license

MiniMax M1 40K is released under the MIT license, which permits commercial use, has 456.0B parameters.

License
MIT
Commercial use allowed
Parameters
456.0B

MIT License - allows commercial use

FAQ

Common questions about MiniMax M1 40K.

When was MiniMax M1 40K released?

MiniMax M1 40K was released on June 16, 2025 by MiniMax. This is the official MiniMax M1 40K release date tracked on LLM Stats.

Is MiniMax M1 40K available via API?

Yes, MiniMax M1 40K is available via API. See the official documentation for authentication and endpoint details.

How big is MiniMax M1 40K?

MiniMax M1 40K has 456 billion parameters. It was trained on 7.5 trillion tokens. It ships as an open-weight model, so you can download and run it on your own hardware.

Who created MiniMax M1 40K?

MiniMax M1 40K was created by MiniMax.

What is the license for MiniMax M1 40K?

MiniMax M1 40K is released under the MIT license. This is an open-source / open-weight license that permits self-hosting.

Where is the MiniMax M1 40K paper or technical report?

MiniMax M1 40K has a paper or technical report available at https://arxiv.org/abs/2506.13585. Use that source for architecture, training, release and evaluation details.