MiniMaxMiniMax M2.7
Minimax M 2.7, developed by Minimax, is a chat model exceling at code generation and reasoning, with a notable context window of 204,800 tokens. It demonstrates strong capabilities in professional software engineering, including log analysis, bug troubleshooting, and system-level reasoning.
Input
Output
Context
205K
Max Output
197K
Parameters
228.7B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Benchmarks
Estimated GPU requirements for MiniMax M2.7
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about MiniMax M2.7
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
MiniMax M2.7 advertises a context window of 204,800 tokens, with a maximum output of 196,608 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. MiniMax M2.7 is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
MiniMax M2.7 accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
MiniMax M2.7 has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured MiniMax M2.7 is shown as not available rather than estimated.
Yes. MiniMax M2.7 is served on the managed pool through the OpenAI-compatible endpoint as minimax-m2-7, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 221.0 GB at INT8 for the weights and a default context, from the catalog's 228.7B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 16, 2026
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