AI Disruption

AI Disruption

Kimi K3 Open-Sourced

Chinese open-source model Kimi K3 shakes Silicon Valley with low cost, high performance, and open weights—sparking fierce debate and industry realignment.

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Meng Li
Jul 28, 2026
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kimi-k3-free · GitHub Topics · GitHub

A Chinese open-source model has triggered an “earthquake” across Silicon Valley tech giants.

Is Kimi K3 Really Better Than Fable?

Is Kimi K3 Really Better Than Fable?

Meng Li
·
Jul 20
Read full story

This familiar plotline last starred DeepSeek R1; this time the protagonist is the newly open-sourced Kimi K3. It has even driven NVIDIA and Anthropic into openly opposing camps.

Kimi K3 Launched: #1 on Frontend, #3 Globally

Kimi K3 Launched: #1 on Frontend, #3 Globally

Meng Li
·
Jul 17
Read full story

Within 30 minutes of being uploaded to the open-source large-model community Hugging Face, Kimi K3 racked up more than 4,000 likes and topped the trending list. Even Hugging Face’s co-founder posted on X that this was the fastest model-release growth ever seen.

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If a company needs to choose a model for local deployment, Chinese models now appear to be the default option. According to data compiled by AI World, Kimi K3’s pricing is far lower than that of the Claude series, yet its evaluation scores are about to catch up with Opus 5.

Frontier-level capability + extremely low price + open model weights—the combined destructive power of these three factors has completely blown the lid off Silicon Valley. The debate around Kimi K3 has expanded from pure model performance all the way to questions of whether open weights are safer and how open-weight models should be handled.

NVIDIA announced the launch of the Open Secure AI Alliance, bringing in more than thirty founding partners including Microsoft, Hugging Face, Cloudflare, and CrowdStrike. Anthropic CEO Dario Amodei, meanwhile, published a lengthy essay explaining why he did not sign the industry open letter supporting open weights.

“Open source” has once again become a prized term that everyone is scrambling to claim.

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