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On our own GPUs

MiniMax M3 API

229.9B MoE that puts frontier coding, a 1M window and native multimodality in one model.

  • Context

    1M

  • Max output

    128K

  • Serving precision

    BF16

  • Licence

    Open weights

MiniMax M3 was released with open weights in June 2026: a 229.9B-parameter mixture-of-experts with 256 experts and 9.8B active per token, extending context from 256K to 1 million tokens. MiniMax describes it as the first open-weight model to combine frontier-level coding, a million-token window and native multimodality across text, image and video input. Its new MSA sparse attention reports roughly a 9.7× prefill and 15.6× decode speedup at 1M context over the previous generation.

Open weights, deployed in facilities we build out and lease directly, operated by us. We resell nobody else’s account quota.

Specifications

Model ID
minimax-m3
Published by
MiniMax
How it is served
On our own GPUs
Licence
Open weights
Total parameters
229.9B MoE
Active parameters
9.8B / 256 experts
Context
1M
Max output
128K
Serving precision
BF16
Input modalities
Text · Image · Video
Released
2026-06-01

What it is good at

It reads video directly
Text, image and video input are all base capabilities. Analysing screen recordings, surveillance clips or product demos does not need a separate frame-extraction-and-captioning pipeline in front.
Long context was re-engineered, not just extended
MSA sparse attention rebuilds attention for very long sequences — the vendor reports ~9.7× faster prefill and ~15.6× faster decode at 1M context. On long-document work that difference decides whether the experience is usable at all.
Unusually balanced for an open model
Coding, long context and multimodality normally force a trade-off across different models. M3 puts them in one weight set, so a multimodal agent needs one fewer model to operate.

Where it fits

  • Multimodal agents that look before they act
  • Automated testing and QA with screenshot or recording evidence
  • Structured summaries of long videos and meetings
  • Mixed coding-plus-vision pipelines, with fewer models to maintain

Three steps

Change one line of base_url, put the model ID below in the `model` field, and leave the rest of your code alone.

model: "minimax-m3"

curl https://api.nexaix.net/v1/chat/completions \
  -H "Authorization: Bearer $NEXAIX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax-m3",
    "stream": true,
    "messages": [
      { "role": "user", "content": "Summarise this contract." }
    ]
  }'

About this model

Is the context 512K or 1M?

The vendor states the window extends from 256K to 1M tokens with a guaranteed floor of 512K. The number published on this page is what we actually serve — treat that as authoritative.

How is video input billed?

Video is billed as the image tokens of its sampled frames, so frame rate and resolution drive cost directly. The docs list recommended parameters; estimate on a short clip before scaling up.

M3 or Kimi K3 for multimodal work?

Both are natively multimodal. K3 is far larger and stronger on hard reasoning; M3 activates only 9.8B, so unit cost is lower and long-context throughput is higher. For vision-heavy batch work that is not reasoning-extreme, M3 is usually the better economics.

Also worth a look

Run your own eval on it

Take the trial credit, run the evals you already have against this model, and then decide whether it gets your production traffic.