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MoonshotAI: Kimi K2 0905 (exacto)

Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). 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.

Context Window

262K

Input Price

$0.6000/M tokens

Output Price

$2.50/M tokens

Modality

text->text

Pricing Breakdown
Cost per million tokens for input and output
Input$0.6000/M tokens
Output$2.50/M tokens

Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $0.008500

Context Window Size
Maximum tokens the model can process in a single request
Context Window262K tokens
26% of 1M max

~197K

Words Equivalent

~131

Pages of Text

~2621

Code Functions

Best Use Cases
What this model excels at
CodingProgrammingDevelopmentMathReasoningAnalysis

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