MoonshotAI: Kimi K2.7 Code

Kimi K2.7 Code is a coding-focused model from Moonshot AI, part of the Kimi K2 family. Moonshot AI built it to complete end-to-end programming tasks reliably over long contexts, and it uses a native multimodal mixture-of-experts architecture. It sits in the text category on api14, and the provider describes it explicitly as a coding model rather than a general-purpose chat model.

The model accepts text and image input, and it produces text output, so you can send screenshots, diagrams or other visual material alongside your written prompt. It supports a context window of 262,144 tokens and a maximum output of 235,929 tokens. That combination of a large context and a large output ceiling means you can feed in substantial codebases or long documents and still receive a lengthy, complete response rather than a truncated one. Because it is a reasoning-capable model, it can also expose its reasoning through the relevant parameters.

The practical uses follow from those capabilities. The long context suits tasks that involve an entire repository or several large files at once, such as refactoring across modules, tracing a bug through many layers, or reviewing a codebase for inconsistencies. Image input helps when the relevant information is in a screenshot of an error, a UI mockup, a whiteboard sketch or a diagram of system architecture. The generous output limit is useful for generating full files, multi-file changes or detailed explanations that would otherwise be cut short. Reasoning support fits problems that benefit from step-by-step analysis before the final answer.

You call it through api14's OpenAI-compatible API using the model id "moonshotai/kimi-k2.7-code". Because the interface matches OpenAI's, you can use existing OpenAI SDKs and point them at api14. The model supports a wide set of request parameters: temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, max_tokens, stop, seed, logit_bias, logprobs, top_logprobs, response_format, structured_outputs, reasoning, include_reasoning, tools, tool_choice and parallel_tool_calls. Tool calling and structured outputs are available, which makes it straightforward to wire the model into agent loops or pipelines that expect machine-readable results.

The facts do not list specific limitations, so the main consideration is fit. It is positioned as a coding model, so for tasks that are not code-related, another model in the catalogue may be a better match. Its image input supports visual context, but output is text only, so it will not produce images. If your task depends on audio or video input, or on non-text output, look elsewhere.