Moonshot AI's Kimi K3 to launch with 2-3 trillion parameters, targets Anthropic Claude Opus 4.8 performance
Moonshot AI will release Kimi K3 in the coming days with a parameter count between 2 trillion and 3 trillion, according to Financial Times sources. The open-weight model is expected to perform at par with or surpass Anthropic's Claude Opus 4.8, making it the largest open-weight AI model from China.
Moonshot AI's Kimi K3 to launch with 2-3 trillion parameters, targets Anthropic Claude Opus 4.8 performance
Moonshot AI will release Kimi K3 in the coming days with a parameter count between 2 trillion and 3 trillion, according to anonymous sources cited by the Financial Times. The open-weight model is expected to perform at par with or potentially surpass Anthropic's Claude Opus 4.8.
Kimi K3 will be the largest open-weight AI model released from China to date. The company's previous Kimi K2 models have performed well on benchmarks and ranked high in the open-source AI market, though specific benchmark scores were not disclosed.
Funding and valuation
Moonshot AI is reportedly raising fresh capital in a round that would value the company at $31.5 billion. The company raised $2 billion at a $20 billion valuation in May 2025, marking a 57.5% increase in valuation over approximately 14 months.
Market context
The release comes as enterprises debate whether to pay premium prices for closed-source models from OpenAI and Anthropic versus using open-source alternatives. Industry executives have expressed concerns about data privacy with closed-source AI products like ChatGPT and Claude, where clients submit proprietary data for processing.
Open-source models from Chinese AI labs including DeepSeek, Z.ai, and Moonshot are increasingly positioned as alternatives that companies can train for specific use cases. The performance gap between these open models and frontier closed-source models has narrowed, according to industry sources.
What this means
If Kimi K3 delivers on claimed performance parity with Claude Opus 4.8, it would represent a significant shift in the open-weight model landscape. A 2-3 trillion parameter open-weight model matching frontier closed-source performance could accelerate enterprise adoption of self-hosted AI solutions, particularly among organizations prioritizing data sovereignty. However, actual benchmark scores and deployment costs remain undisclosed, making it difficult to assess the true competitive position against Anthropic's offering.
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