Call Kimi K3 with the OpenAI SDK
Kimi K3 supports the Chat Completions request format, so developers can use the OpenAI SDK to call it through Snowflake's Cortex REST API. Set SNOWFLAKE_PAT to your Snowflake programmatic access token and SNOWFLAKE_ACCOUNT_URL to your account's HTTPS URL.
Kimi K3, Moonshot AI's open-weight model, is now available in private preview on Snowflake Cortex AI. Adding K3 to Cortex AI gives teams another option for matching the right model to the job, whether that means frontier reasoning for complex tasks or reserving higher-cost proprietary models for where they make the biggest difference.
At launch, preview customers can use Kimi K3 through Snowflake Cortex AI Functions and Snowflake Cortex Inference. Support for Snowflake CoCo, Snowflake Cortex Agents, and Snowflake CoWork is coming soon.
In Moonshot's published evaluations, K3 autonomously built a working GPU compiler from scratch, reproduced complex astrophysics research in two hours, and sustained multi-day coding sessions across large repositories. These results point to a model designed for work that takes many steps, uses more than one kind of input, and needs to carry context forward. The sections below cover what that looks like on Snowflake.
Kimi K3: designed for long-running work
Kimi K3 is a 2.8-trillion-parameter mixture-of-experts model that activates 16 of 896 experts per token using Moonshot's Stable LatentMoE framework. Moonshot reports a 2.5x improvement in scaling efficiency over its predecessor, K2.
In practice, that architecture targets work that takes several steps and uses more than one kind of input. Moonshot highlights long-running coding sessions that navigate large repositories, orchestrate terminal tools, and use screenshots to refine the result. Its research examples combine literature review, executable code, validation, and interactive visualizations. For teams, that maps to repository-level development, research synthesis, and analytical workflows where the model needs to carry context forward across many turns.
These examples describe model-level capabilities. The inputs, tools, context limits, and output handling available to you depend on the Snowflake surface and configuration you use.
Sources: Moonshot AI, Kimi K3: Open Frontier Intelligence and Hugging Face
What you can do with Kimi K3 on Snowflake
Kimi K3 adds an open-weight model option to Snowflake's model selection. During private preview, teams can evaluate it against their own tasks and quality requirements without setting up a separate inference deployment.
Coming soon, support for Kimi K3 in Snowflake CoCo, Snowflake CoWork, and Cortex Agents will extend that evaluation to coding, research, and data agent workflows:
- Develop with Snowflake CoCo: Repository-level work depends on more than the file in front of you. A developer may need to trace a call through several modules, understand the existing tests, and carry a change through to a working result. Kimi K3's focus on long-running coding makes this a useful area to evaluate in CoCo.
Start a fresh session with Kimi K3 and give it a bounded task with explicit acceptance criteria. Moonshot recommends against switching an ongoing session from another model to K3 because the model is sensitive to how conversation history is preserved. - Bring research into data agents: With support in Snowflake CoWork and Cortex Agents, teams will be able to evaluate research and analysis workflows that draw on the data and tools available to their agent. For example, use Kimi K3 to compare customer research notes, identify conflicting requirements, and prepare a brief with the evidence behind each recommendation.
Benchmark results
The following snapshot showcases results from Moonshot AI's published evaluation table including coding, agentic, reasoning, and visual benchmarks. For full methodology, harness details, and footnotes, see Moonshot AI's Kimi K3 model card.