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Full text: (Archive link: https://archive.ph/Sy4tV)

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Alibaba-backed Chinese artificial intelligence startup Moonshot just unveiled its latest model, Kimi K3, and it’s already sending shockwaves through the industry, with some benchmarks showing the model outperforming Anthropic and OpenAI’s best offerings.

The model packs 2.8 trillion parameters, which Moonshot says would make it the largest open-weight model released to date once its weights become available by July 27.

In a blog post, the company acknowledged that K3’s overall performance still trails Claude Fable 5 and GPT-5.6 Sol. Its internal evaluations nevertheless place it close to both models on several tasks, while independent testing by Artificial Analysis ranks it immediately behind the leading proprietary systems on its Intelligence Index and real-world work evaluations.

On Arena.ai’s front-end development leaderboard, K3 even ranks above the two most powerful models, marking a 17-place jump from the company’s previous model, Kimi K2.6. Arena’s CEO, Anastasios Angelopoulos, said Kimi K3 “may be the single biggest release of the year” and “the moment that OSS Chinese models have surpassed US models,” in a post on X.

Anastasios Nikolas Angelopoulos (@ml_angelopoulos) [https://xcancel.com/ml_angelopoulos/status/2077832882673066109]

This may be the single biggest release of the year, and marks the moment that OSS Chinese modles have surpassed US models.

Code Arena, Kimi K3 has BEATEN FABLE.

This is only 6 weeks after the Fable release.

This makes @Kimi_Moonshot the #1 AI lab in the world on frontend coding capability, and more results are rolling in that are likely to continue to show it is at the top of the pack.

The implications of this, whether on the closed-source AI business models or the larger capital ecosystem in the US, are enormous.

Arena.ai (@arena) [https://xcancel.com/arena/status/2077824029126504525]

Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.

This is a 17-place jump from Kimi-k2.6 (#18 -> #1).

In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.

The full model weights will be released by July 27.

Congrats to the @Kimi_Moonshot team on this major milestone!

It’s a remarkable achievement, especially for an open-source model. The results challenge the assumption that China’s leading AI labs remain several months behind their American competitors. Anthropic just released Fable 5 last month, while OpenAI’s GPT-5.6 (and its three tiers, Sol, Terra, and Luna) just dropped last week.

“Kimi k3 is a big moment with multiple implications for the entire industry,” Trump’s former senior White House policy advisor on AI, Sriram Krishnan, said in a post on X.

The last time something like this happened, aka when a Chinese AI lab released a cheaper model that proved competitive with American alternatives, was when DeepSeek released R1 back in January 2025. Following that release and its reception, the market reaction helped wipe roughly $1 trillion from global technology stocks. Meanwhile, the model’s success raised major national security concerns across Washington D.C., and partially informed the Trump administration’s hard-line stance on advanced tech exports to China.

Moonshot’s release also comes only a few months after Anthropic accused the company, along with other Chinese AI companies DeepSeek and MiniMax, of violating their rules to “illicitly” extract the capabilities of its model Claude and use that to improve their own models. The process is called “distillation,” and it’s fairly common in the industry, but the Trump administration has deemed it “adversarial” and vowed to crack down on it.

K3 arrives amid heightened scrutiny of the U.S.-China AI race and growing national-security concerns around frontier models. Its release is likely to renew debate in Washington over export controls, distillation, and whether restrictions on Chinese labs are slowing their progress at all.

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The researchers trained a trillion-parameter model using zero reinforcement learning to see how reasoning capabilities emerge at a massive scale without relying on human-annotated data. They found that pushing the parameter count to a trillion drastically improves both sample efficiency and the overall performance ceiling when compared to a smaller 104-billion parameter baseline strongly validating the concept that raw scale and computation eventually outpace hand-crafted human heuristics.

They also discovered that the training process reliably unfolds in two distinct sequential stages. The model starts with a discovery phase where it actively expands its reasoning boundaries by unlocking dormant pathways, and then it moves into a sharpening phase where it refines its policy within those established limits. Notably, the model spontaneously developed advanced cognitive strategies entirely on its own.

It began using structured formatting, parallel reasoning, self-verification, context anxiety, and even anthropomorphic expressions of frustration during complex tasks without any explicit human prompting. To keep the training stable at such a massive scale, the team relied on simple optimization techniques like clipped importance sampling and mixed-precision control. They also created a new evaluation framework to judge the actual quality of the reasoning steps based on comprehensibility, reproducibility, and token efficiency instead of just looking at the final answer.

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Instead of just throwing massive compute at the wall and hoping for the best, the authors of the paper approached the entire process as a system-level optimization problem with a setup they call a hill-climbing machine. The end result is a Mixture-of-Experts model with 35 billion active parameters and 1 trillion total parameters. It performs incredibly well on tough math and coding benchmarks, scoring 97.0% on AIME 2025 and 52.8% on SWE-Bench Pro.

They trained MAI-Thinking-1 entirely from scratch using 30 trillion tokens of clean human-generated data and explicitly avoided distilling data from other frontier models or using scrubbed AI-generated content from their datasets. The team argues that while copying other models is faster, building capabilities from the ground up creates a more robust and steerable system. To handle this massive undertaking, they built a custom training framework called YOLO to run on up to 8000 GB200 GPUs which allowed them to control everything from the hardware kernels to the network communications.

Instead of trying to teach one model everything at once, they branched the training into three separate paths. One specialist for math and science, one for agentic coding and tool use, and one strictly for safety and helpfulness. Once these specialists were fully baked, they merged their knowledge together using a supervised fine-tuning process. A final lightweight reinforcement learning run polished the consolidated agents in to the final model.

The model holds its own against Claude Sonnet 4.6 across various benchmarks, and they also ran extensive human evaluations to ensure the model was actually helpful in real-world scenarios rather than just optimized for automated tests.

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