Kimi K2 Instruct 0905
Built by Moonshot AI · China · moonshot.ai
Kimi K2 0905 is the September update of Kimi K2 0711. It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It supports long-context inference up to 256k tokens, extended from the previous 128k. This update improves agentic coding with higher accuracy and better generalization across scaffolds, and enhances frontend coding with more aesthetic and functional outputs for web, 3D, and related tasks. Kimi K2 is optimized for agentic capabilities, including advanced tool use, reasoning, and code synthesis. It excels across coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench) benchmarks. The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.
How to use
Just add the suffix. Hard questions still come back from this model; only easy ones drop to something cheaper, and nothing pricier than it gets used.
model: "moonshot/kimi-k2-instruct-0905:auto"
Sets no behavior — whatever your key already stores stays in effect.
Without the suffix, the bare name always goes to this model — no routing, so nothing saved.
Context
Max output
Input $/1M
Output $/1M
Features
Providers
GMI CloudProviders
Price, latency, and uptime per provider serving this model. Warp tries them in order of how each host has just been behaving, moving to the next on failure. Click a row for regions and data policies.
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Performance
Benchmark quality scores, and measured speed per provider.
Speed
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Parameter support
What Warp actually does with each parameter when you call this model. The answer differs by serving provider and by API surface.