MoonshotAI: Kimi K2 Thinking

Kimi K2 Thinking is a text model from Moonshot AI, identified on api14 as moonshotai/kimi-k2-thinking. It belongs to the K2 series and is described by its provider as Moonshot AI's most advanced open reasoning model to date, extending that series into agentic, long-horizon reasoning. It is built on the trillion-parameter Mixture-of-Experts architecture introduced in the earlier K2 work. The provider release date is 6 November 2025.

The model accepts text input and returns text output, so it is a good fit for language tasks rather than anything involving images or audio. Its context window is 262,144 tokens, which is large enough to hold long documents, extended conversation histories or sizeable codebases in a single request. The maximum output is 98,304 tokens, so responses can themselves be long, which matters for tasks where the model needs room to reason before answering. Because it is a reasoning model, it can produce reasoning content alongside its final answer, and this behaviour can be controlled through the relevant request parameters.

Those capabilities point to a fairly specific set of uses. Long-horizon reasoning and agentic behaviour suit multi-step tasks where the model has to plan, call tools and keep working toward a goal across many turns. The large context window helps with analysing long reports, reviewing large diffs or code files, and summarising or extracting structured information from lengthy inputs. Tool use and structured outputs make it usable inside pipelines that need the model to call functions or return data in a defined shape, and the reasoning output is useful when you want to inspect how an answer was reached rather than only seeing the result.

You call it through api14's OpenAI-compatible API using the model id moonshotai/kimi-k2-thinking, which means existing OpenAI client libraries and request shapes work with only the base URL and key changed. The supported parameters 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 that covers sampling controls, determinism via seed, stopping conditions, tool and function calling, structured output formats, and options for exposing or tuning the reasoning content. Set max_tokens with the 98,304 output ceiling in mind.

There are cases where another model may fit better. This is a text-only model, so any task that needs image or audio input falls outside its scope. It is also a reasoning model with a very large context window, which is not always what you want for simple, high-volume or latency-sensitive work where a smaller, faster model would do. If your task is short and straightforward, or if you need multimodal input, look elsewhere; if you need long-context reasoning, tool use or structured output, this model is the appropriate choice.