MiniMax: MiniMax M2.5

MiniMax M2.5 is a text model from MiniMax, listed on api14 under the model id minimax/minimax-m2.5. MiniMax describes it as a state of the art large language model built for real world productivity, trained across a diverse range of complex real world digital working environments, and it builds on the coding expertise of M2.1. It sits in the text category, so it is aimed at language work rather than image, audio or video generation.

The model takes text as input and returns text as output. It has a context window of 204,800 tokens and can produce up to 128,000 tokens in a single response, which is enough room for long documents on the input side and lengthy, detailed answers on the output side. Among the parameters it supports are temperature, top_p, top_k, min_p, repetition_penalty, frequency_penalty and presence_penalty for controlling sampling, plus max_tokens and stop for shaping the length and ending of a response. It also supports reasoning and include_reasoning, which matter when you want the model to work through a problem before answering.

Because the provider positions M2.5 around real world productivity and coding, the natural fits are software tasks such as code generation, refactoring, debugging and explanation, along with the general knowledge work that surrounds them: drafting and editing long documents, summarising large inputs, extracting structured data, and answering questions over material that fits inside the context window. The large output allowance suits jobs where the answer itself is long, for example producing a full file, a detailed report or a multi part plan in one call. Tool use and structured outputs make it a reasonable choice for agent style workflows where the model has to call functions or return data in a fixed shape.

You call it through api14's OpenAI-compatible API by setting the model field to minimax/minimax-m2.5 in a chat completion request, using the same client libraries and request shape you would use with OpenAI. The supported parameters cover the usual sampling controls (temperature, top_p, top_k, min_p, repetition_penalty, frequency_penalty, presence_penalty), length and stopping controls (max_tokens, stop), determinism and inspection options (seed, logit_bias, logprobs, top_logprobs), output shaping (response_format, structured_outputs), and tool use (tools, tool_choice). Reasoning behaviour is exposed through reasoning and include_reasoning, so you can decide whether reasoning content is returned alongside the final answer.

Since the model accepts only text, tasks that depend on images, audio or other non text inputs are outside its scope and would need a model with those input modalities. The same applies if you need a non text output. For text workloads that fit the context and output limits above, minimax/minimax-m2.5 is a straightforward option to evaluate, and the parameter list gives you enough control to tune sampling, enforce structure and wire it into tool calling pipelines.