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Kimi-K2.6 is a nextâgeneration language model that builds upon the successes of its predecessors with notable improvements in reasoning and multilingual capabilities. It employs a refined transformer architecture featuring sparse attention mechanisms that reduce computational load while preserving longârange dependencies. The model was trained on an extensive corpus of over 5 trillion tokens, encompassing code, scientific literature, and diverse conversational data. With a parameter count of 180â¯billion and a context window of 8â¯K tokens, Kimi-K2.6 achieves stateâofâtheâart performance across benchmark suites. The model specifications are summarized in the table below:
| Parameters | 180â¯B |
| Context Length | 8â¯K tokens |
| Training Tokens | 5â¯trillion |
| Architecture | Transformer with sparse attention |