MiniMax M3 API

MiniMax M3 is an open-weight large language model from MiniMax (MiniMaxAI), released June 1, 2026. It targets long-horizon coding and agent workloads: autonomous task decomposition, tool use, and multi-step reasoning across a 1M-token context. Its defining change is MiniMax Sparse Attention (MSA), which selects the key–value blocks that matter instead of attending to every token — the reason a 1-million-token window is practical to run rather than just a spec-sheet number. On GPTProto you call the MiniMax M3 API through one account balance shared with 200+ other models, no separate MiniMax sign-up required.

Spec table：

MiniMax M3 vs MiniMax M2.5

Both models run on GPTProto under the same key and balance. M2.5 is the earlier, full-attention text model; M3 moves to sparse attention (MSA) and a practical 1M-token window, and adds image input through its image-to-text subpage.

Switching from the official MiniMax API

If you already call MiniMax directly, moving to GPTProto is a drop-in change: point your client at the GPTProto endpoint, pass your GPTProto key, and set the model to MiniMax-M3. The request and response shape follow the OpenAI chat format, so existing code paths stay the same. You keep one balance across 200+ models, skip a separate MiniMax platform sign-up, and avoid the regional payment friction Western developers hit on the official Shanghai platform.

One migration gotcha: GPTProto expects the API key directly in the Authorization header — no Bearer prefix. If your OpenAI SDK auto-adds Bearer, set the header manually.

bash

curl --location 'https://gptproto.com/v1/chat/completions' \
  --header 'Authorization: GPTPROTO_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "MiniMax-M3",
    "messages": [{ "role": "user", "content": "Who are you?" }],
    "stream": false
  }'

Is MiniMax M3 open source?

Yes. MiniMax released M3 as an open-weight model, with weights and a technical report published to Hugging Face and GitHub. On GPTProto you can call the hosted MiniMax M3 API without self-hosting — useful when you want the model's long-context and agent behaviour but not the GPU footprint of running 229.9B parameters yourself.