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MiniMax M3: open weights land for the 428B multimodal model with 1M context

MiniMax published the open weights of M3, a 428B-total / 23B-active natively multimodal MoE with a 1M-token context, after its June API launch.

trendingmodels · open-weightsJEV traction 0.67added 2026-11-05
Open MiniMax M3 →View on the radar

Why it matters

The weights drop turns June's API-only flagship into a downloadable model: 1M context, native text-image-video training from step zero, and a 12-hour autonomous ICLR paper replication demo, now runnable locally via vLLM and llama.cpp.

What you could build with it

A startup building a long-document research assistant could run M3 locally with its 1M-token context to ingest whole codebases or paper collections in one window, avoiding per-token API bills while keeping data on its own cluster.

Does it hold up?

Usable now through the hosted API and community quants: Unsloth's 2-bit GGUF runs on roughly 150GB of RAM, but full weights are 428B, and the MiniMax Community License requires written authorization for companies over $20M in yearly revenue.

Built with MiniMax M3

Learn more

licence and weights explainer →

First spotted on article: source.

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