MoonshotAI: Kimi K2 0711

Kimi K2 0711 is a text model from Moonshot AI, identified on api14 as moonshotai/kimi-k2. It is a large-scale Mixture-of-Experts (MoE) language model with 1 trillion total parameters and 32 billion active per forward pass, which means only a fraction of the network is engaged for each token it generates. The provider release date is 2025-07-11, and the model's knowledge cutoff is 2024-12-31, so anything after that date is outside what it learned during training.

The model works with text in and text out. It accepts a context window of 131,072 tokens and can produce up to 98,304 tokens in a single response, which is a large output budget relative to most chat models. That combination suits workloads where the input is long (documentation, transcripts, codebases) and the expected answer is also long, such as a full report or a substantial refactor rather than a one line reply.

Because it supports tool calling, Kimi K2 fits agent style workflows where the model decides when to invoke a function and what arguments to pass. Typical uses include multi step assistants that query internal APIs, retrieval augmented generation over large document sets, long form summarization and drafting, and code generation or review where the model needs to hold a lot of surrounding context. The high output ceiling also makes it reasonable for tasks that would otherwise need to be split across several calls.

You call it through api14's OpenAI-compatible API using the model id "moonshotai/kimi-k2", so existing OpenAI SDKs and clients work with only the base URL and key changed. The request parameters it supports are frequency_penalty, max_tokens, presence_penalty, repetition_penalty, seed, stop, temperature, tool_choice, tools, top_k and top_p. The presence of seed allows more reproducible sampling, and tools plus tool_choice let you define function schemas and control whether the model must call one.

Keep the limits in mind when choosing it. Input and output are text only, so image or audio tasks need a different model, and the 131,072 token context and 98,304 token output cap bound how much can be handled in one request. The knowledge cutoff of 2024-12-31 means recent events are not covered unless you supply them in the prompt or through tools. For short, simple requests, a smaller model may be a better fit.