MoonshotAI: Kimi K2.6

Kimi K2.6 is a text generation model from Moonshot AI, listed on api14 under the model id moonshotai/kimi-k2.6. It sits in the provider's Kimi line, and Moonshot AI positions it as a next-generation multimodal model built for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. The provider release date is 2026-04-20.

The model accepts text and image inputs and returns text. Its context window is 262,144 tokens, with a maximum output of 235,929 tokens, which makes it suitable for workloads where a large amount of source material is supplied alongside the instruction. Moonshot AI describes it as handling complex end-to-end coding tasks across Python, Rust, and Go. Because images can be part of the input, a request can combine a screenshot, diagram, or design mockup with written instructions and receive text back.

Those capabilities point to a few practical uses. Long-horizon coding work fits the large context and output allowance, since a request can carry substantial repository context and produce extended results. Coding-driven UI and UX generation follows from the combination of code ability and image input: a developer can supply a visual reference and ask for markup or component code. Multi-agent orchestration is another intended use, where the model coordinates other agents or tools rather than only answering a single prompt. Mixed text and image work, such as reading a design or diagram and describing or transforming it in text, is also supported by the input modalities.

To call the model through api14's OpenAI-compatible API, use the model id moonshotai/kimi-k2.6. The supported request parameters are frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, parallel_tool_calls, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, and top_p. Tool use and structured outputs are therefore available directly through the request, along with sampling controls and reasoning options such as include_reasoning and reasoning.

Because it is a text output model, anything that requires generated images or audio is out of scope here; another model would be needed for those output types. Within text work, the main constraint to keep in mind is the gap between input and output. The context window is 262,144 tokens while the maximum output is 235,929 tokens, so long responses consume a large share of the available space and leave less room for input. If a task needs only a short answer over a small prompt, the full context is not necessary, but the model still supports it.