Xiaomi: MiMo-V2.6-Pro

Xiaomi: MiMo-V2.6-Pro is the flagship foundation model from Xiaomi, built at a scale of over 1T parameters. It sits in the text category and is positioned by its maker as a model designed to push the ceiling of capability for the most demanding work. If you are evaluating models for a production workload, this is the top tier option in Xiaomi's MiMo line rather than a lightweight or budget variant.

The model accepts text, image, video and audio as input, and returns text. That combination makes it multimodal on the input side while keeping generation text only, so it fits pipelines where you feed in mixed media and expect written analysis, summaries or answers back. The context window is 1,050,000 tokens, which is large enough to hold very long documents, long transcripts or extended media-derived context in a single request. Maximum output is 131,072 tokens, so long-form generation is practical without chaining many calls.

Those capabilities point to a few concrete uses. Long-context document work is an obvious fit: contract review, research synthesis, or reasoning over large codebases where the whole corpus needs to stay in view. Because images, video and audio are accepted as input, you can build workflows that summarise recorded meetings, describe or query video content, or extract structured information from scanned documents and screenshots. Tool calling and structured outputs make it usable as the reasoning core of an agent that needs to call functions and return machine-readable results, and the reasoning parameter lets you surface intermediate thinking when a task benefits from it.

You call it through api14's OpenAI-compatible API using the model id "xiaomi/mimo-v2.6-pro". Because the interface is OpenAI-compatible, existing clients and SDKs work with a base URL change. The model supports frequency_penalty, presence_penalty, repetition_penalty, temperature, top_p, top_k, min_p, seed, stop, logit_bias, max_tokens, response_format, structured_outputs, tools, tool_choice, reasoning and include_reasoning. Sampling controls, determinism via seed, and tool or schema-constrained output are all available, so you can tune behaviour per request rather than accepting fixed defaults.

The main thing to weigh is scope. This is a flagship model, and the supported parameter list covers text generation controls only; output is text, so tasks that need generated images, audio or video are out of scope here. If your workload is short prompts with modest context, a smaller model may be the more sensible default, and this one is better reserved for cases that actually use the long context or multimodal input.