Xiaomi: MiMo-V2.5-Pro

Xiaomi: MiMo-V2.5-Pro is a text model from Xiaomi. It is the company's flagship model, positioned around general agentic capabilities, complex software engineering, and long-horizon tasks. Xiaomi reports top rankings for it on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro. It is a text-in, text-out model, so it works with written prompts and returns written responses.

The model accepts text input and produces text output, with a context window of 1,050,000 tokens. That is large enough to hold entire codebases, long document sets, or extended agent transcripts in a single request. The maximum output length is 131,072 tokens, which supports long generated answers rather than short replies only. Reasoning is part of the model's feature set: the supported parameters include reasoning and include_reasoning, so you can control or surface the model's reasoning behaviour where your client supports it.

Those capabilities point to a few practical uses. Agentic workflows are a natural fit, since the model is built for general agent behaviour and long-horizon tasks that span many steps. Complex software engineering is another, including work over large repositories where the context window matters. Because output can run to 131,072 tokens, the model also suits tasks that need long, structured deliverables rather than brief answers. Structured outputs and tool calling make it usable in pipelines where the model must return machine-readable data or invoke external functions.

You call it through api14's OpenAI-compatible API using the model id "xiaomi/mimo-v2.5-pro". The request parameters it supports are frequency_penalty, include_reasoning, logprobs, max_tokens, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, and top_p. In practice, temperature, top_p, and top_k shape sampling; max_tokens and stop bound the response; seed adds reproducibility; frequency_penalty, presence_penalty, and repetition_penalty discourage repetition; response_format and structured_outputs constrain the shape of the output; and tools plus tool_choice enable function calling. logprobs and top_logprobs return token-level probability information, and reasoning plus include_reasoning relate to the model's reasoning output.

Considerations are mostly about fit. Input is text only, so there is no image, audio, or other modality support; if your task depends on those inputs, a different model is needed. The very large context window and long maximum output are useful but not always necessary, so for short, simple text tasks a smaller or faster model may be a better match. Within those bounds, MiMo-V2.5-Pro is aimed at demanding text work: agents, software engineering, and tasks that run over long horizons.