MiniMax: MiniMax M3.1 Flash

MiniMax M3.1 Flash is a text model from MiniMax, released by the provider on 2026-10-19 under the model id minimax/minimax-m3.1-flash. It sits in the text category and is positioned as a faster variant in the M3 line. MiniMax describes it as a multimodal model that improves on M3 across multiple benchmarks, with particular gains in complex agentic tasks, coding and cybersecurity.

The model accepts text, image and video as inputs and returns text. Its context window is 1,000,000 tokens, and it can produce up to 524,288 output tokens in a single response. That combination of a very large input window and a large output budget means you can feed it long documents, extended conversation histories or media alongside text, and still receive substantial generated output rather than a short reply.

Those capabilities point to a few practical patterns. Long-context work such as summarising or querying large document sets, codebases or transcripts fits the input window well. Because images and video are accepted, you can build pipelines that reason over screenshots, diagrams, recorded footage or mixed media without a separate preprocessing model. The large output allowance suits generation tasks that need long, structured results, and the reasoning and tool parameters make the model a reasonable fit for agentic workflows where it must plan, call tools and act over several steps. Coding and cybersecurity analysis are the areas MiniMax highlights, so those are natural starting points.

Through api14 you call it with the OpenAI-compatible API using the model id minimax/minimax-m3.1-flash, so existing OpenAI SDKs and clients work with a base URL change. The request parameters it supports are include_reasoning, max_tokens, reasoning, reasoning_effort, response_format, temperature, tool_choice, tools and top_p. In practice that means you can control sampling with temperature and top_p, cap or shape output with max_tokens and response_format, and wire up function calling with tools and tool_choice. The reasoning, reasoning_effort and include_reasoning parameters let you tune how much internal reasoning the model does and whether that reasoning is returned to you.

The main thing to weigh is scope. Output is text only, so if you need generated images, audio or other modalities, this model is not the right choice. It is also a Flash variant, which suggests it trades some capability for speed relative to the full M3 model, so for the hardest reasoning problems you may want to compare against a larger sibling. If your workload is purely text and short-context, the million-token window and media inputs are more than you need, and a smaller model may be simpler to operate.