
Moonshot’s upcoming Kimi 3 is expected to close the gap with Anthropic’s Opus 4.8
Quick Answer
Moonshot AI's upcoming Kimi K3 model is set to rival Anthropic's Opus 4.8, with a parameter count between 2 trillion and 3 trillion.
Quick Take
Moonshot AI's upcoming Kimi K3 model is set to rival Anthropic's Opus 4.8, with a parameter count between 2 trillion and 3 trillion. The K3 aims to enhance open-source AI capabilities, addressing concerns over the cost and data privacy of closed-source models. As the company raises capital to reach a $31.5 billion valuation, the shift towards open models gains traction in the industry.
Key Points
- Kimi K3 will be the largest model from China.
- The model's parameter count is expected to be between 2 trillion and 3 trillion.
- Moonshot AI aims to close the performance gap with closed-source models.
- The company is raising capital to achieve a $31.5 billion valuation.
- Industry leaders are advocating for cheaper open-source alternatives.
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The latest iteration of Chinese AI lab Moonshot AI’s Kimi model series is expected to perform at par with or even surpass Anthropic’s Opus 4.8, the Financial Times reported, citing anonymous sources.
Moonshot’s Kimi K2 models have been received well in the open-source AI market, ranking high on benchmarks and demonstrating capabilities that aren’t too far behind the latest frontier models.
The company’s upcoming release, called Kimi K3, is said to take this one step further to close the gap with closed-source models from the likes of OpenAI and Anthropic. The FT reports Kimi K3 will be the largest open-weight AI model from China, with a parameter count between 2 trillion and 3 trillion, and will be released “in the coming days.”
Moonshot is also said to be raising fresh capital in a round that would valuate it at $31.5 billion. The company in May raised $2 billion at a $20 billion valuation.
The news comes amid a fresh debate on the value of paying AI labs like OpenAI and Anthropic for their expensive, closed-source models. Industry leaders fear that AI labs will somehow manage to extract the data their clients submit for use with their AI products like ChatGPT and Claude.
Executives are pitching their own products as alternatives, or recommending companies to take cheaper open source models, like those developed by DeepSeek, Z.ai or Moonshot, and train it for their own purposes. The argument has gained momentum, especially as open models from China close the gap with their more expensive, frontier counterparts.
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