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MiniMax's open-weights multimodal MoE model with a 1M-token context window and configurable reasoning effort.
Overview. MiniMax M3.1 Flash is an open-weights multimodal model from MiniMax. It accepts text, image, and video input and produces text, with a sparse mixture-of-experts architecture of approximately 428B total parameters and 23B active per token. Developers can use it when long-context multimodal input and configurable reasoning effort are important.
Key strengths. The model supports a 1M-token context window and configurable reasoning effort levels from low through max. Its multimodal input covers text, images, and video, while its MoE design routes each token through a subset of the model's experts.
When to use it. Long-context work that combines text with images or video, and workflows where reasoning depth should be adjustable to balance quality against speed.
Limitations. Produces text only, with no audio output. Weights are released under the custom MiniMax Community License.
Sovereignty note. MiniMax is the model creator. Where the model runs and under which terms depends on the provider endpoint.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.27 / 1.09 | Online |
5 use cases classified by the curator. The note says why.
A 1M-token context can hold extensive source material for document-grounded answers, but no retrieval or answer-quality benchmark is available.
The 1M-token context supports question answering over large documents, though no document-QA benchmark or curated quality signal is available.
Tool-calling support fits applications that invoke functions, while the 1M-token context supports workflows with substantial accumulated context.
The 1M-token context enables summarising very long inputs; no summarisation benchmark or curated quality signal is provided.
Tool-calling support directly enables tool orchestration, and the 1M-token context can accommodate extensive tool outputs; no benchmark or other evidence supports a stronger rating.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "minimaxai/minimax-m3-1-flash",
"reasoning_effort": "high",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at