Moonshot AI released Kimi K3, a 2.8-trillion-parameter sparse mixture-of-experts model with native vision and 1-million-token context window, featuring novel attention mechanisms called Kimi Delta Attention and Attention Residuals. The model achieves 6.3x faster decoding in million-token contexts and 25% higher training efficiency, while activating only 16 of 896 experts through Stable LatentMoE sparsity. K3 outperforms other open models on Moonshot's evaluations but remains behind proprietary models like Claude and GPT variants, expanding the scope of openly available large language models.
Simon Willison's Weblog·3 months ago·
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● 7 sources
Moonshot AI released Kimi K3, a 2.8 trillion parameter model available via API with open weights promised by July 27, 2026, positioning it as the first open 3-trillion parameter model. The model costs $3 per million input tokens and $15 per million output tokens, making it the most expensive Chinese AI lab model to date and comparable to Anthropic's Claude Sonnet pricing. The author demonstrates K3's capabilities through a pelican-riding-a-bicycle benchmark test, which generates a 16,658-token response costing 25 cents, while reflecting on how this once-useful comparison metric has diminished in correlation with actual model quality as capabilities have advanced.
Moonshot AI's upcoming Kimi K3 model, expected between 2 trillion and 3 trillion parameters, is projected to match or exceed Anthropic's Opus 4.8 performance according to Financial Times sources. Moonshot is raising fresh capital at a $31.5 billion valuation, up from $20 billion in May, as Chinese open-weight models increasingly close the performance gap with expensive closed-source alternatives from OpenAI and Anthropic. The release is expected in the coming days and reflects growing momentum toward open-source AI models as cost-effective alternatives to proprietary systems.
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