MiniMax M3

Reason over 1M-token context for coding and agents.

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MiniMax M3 — Multimodal Text Generation LLM

What is MiniMax M3?

MiniMax M3 is a frontier open-weight large language model from MiniMax, built for coding, long-horizon agentic work, and long-context reasoning. It pairs a 1M-token context window with native multimodal training across text, image, and video, and returns text output. Under the hood, M3 is a Mixture-of-Experts model with roughly 428B total parameters and about 23B activated per token, so it delivers frontier quality without dense-model compute.

The headline architectural change is MiniMax Sparse Attention (MSA). Instead of comparing every token against every other token, MSA selects the key-value blocks that matter most, cutting per-token compute at long context to roughly one-twentieth of the previous generation while keeping quality steady. On Segmind, you send a single prompt and receive the model's text response synchronously — no polling required.

Key Features

  • •1M-token context window for whole-repository code, long documents, and multi-file reasoning.
  • •MiniMax Sparse Attention (MSA) for fast prefill and decode at extreme context lengths.
  • •Agentic and coding strength, with reported frontier-range scores on SWE-Bench Pro, Terminal-Bench, and BrowseComp.
  • •Interleaved thinking that lets the model plan, act, and verify across long tool-driven workflows.
  • •Synchronous text output through a clean single-prompt API on Segmind.

Best Use Cases

MiniMax M3 shines when tasks are long and multi-step rather than single-turn. Use it for multi-file refactors, bug fixes, and feature implementation; for agent loops that read files, call tools, and iterate; and for analyzing large codebases, logs, transcripts, or long documents in one pass. Its structured, low-filler responses make it a strong fit for technical analysis and detailed reports.

Prompt Tips and Output Quality

Put your instruction after large context with a bridging line such as "Based on the material above." Be explicit about role, tools, and the output format you want (JSON, structured Markdown, or a unified diff). For agent workflows, ask the model to plan before each step and verify after. Keep only the context the task needs — a huge window rewards relevance, not raw volume.

FAQs

Is MiniMax M3 good for coding? Yes. It reaches frontier-range coding and agentic benchmarks and is designed for long-horizon software tasks.

What context length does it support? Up to 1,000,000 tokens, making full-repo and long-document reasoning practical.

Is it multimodal? M3 is natively trained on text, image, and video inputs; the Segmind endpoint accepts a text prompt and returns text.

How large is the model? It is a Mixture-of-Experts model with about 428B total parameters and roughly 23B active per token.

How do I get the best results? Provide clear roles, explicit output formats, and only the relevant context, then place your instruction last.