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Kimi K3
The world's first open 3T-class model
Kimi K3 is a 2.8T-parameter open model featuring native vision capabilities, a 1-million-token context window, and Moonshot AI's Kimi Delta Attention and Attention Residuals architectures. Built as the world's first open 3T-class model, it delivers frontier-level performance in long-horizon coding, compiler development, digital creation, and scientific reasoning, outperforming previous open models in scaling efficiency and agentic capabilities.
While most of the industry is focused on scaling compute, Moonshot is focused on scaling intelligence.
Instead of just scaling up model parameters (which they did anyway—hitting a massive 2.8T parameters!), Kimi K3 introduces a 2.5x improvement in scaling efficiency using their custom Kimi Delta Attention and Attention Residuals architectures.
It is the world’s first open 3T-class model, and its long-horizon agentic workflows are wild:
🤯 1M Context + Native Vision: Built for massive data ingestion, from video and screens to complex systems.
💻 Autonomous Engineering: It built its own GPU compiler (MiniTriton) and optimized complex GPU kernels competitively with the strongest proprietary models.
🧠 Chip Design & Astrophysics: In a single 48-hour run, it autonomously designed and verified its own microchip. It also bridged astrophysics literature with executable code to reproduce complex stellar relations.
It's impressive to see a 2.8T model with this level of long-horizon reasoning being open-sourced.
How do you see open-source weights of this scale shifting the balance with proprietary AI?
About Kimi K3 on Product Hunt
“The world's first open 3T-class model”
Kimi K3 was submitted on Product Hunt and earned 51 upvotes and 19 comments, placing #11 on the daily leaderboard. Kimi K3 is a 2.8T-parameter open model featuring native vision capabilities, a 1-million-token context window, and Moonshot AI's Kimi Delta Attention and Attention Residuals architectures. Built as the world's first open 3T-class model, it delivers frontier-level performance in long-horizon coding, compiler development, digital creation, and scientific reasoning, outperforming previous open models in scaling efficiency and agentic capabilities.
On the analytics side, Kimi K3 competes within Open Source, Artificial Intelligence and Development — topics that collectively have 548.4k followers on Product Hunt. The dashboard above tracks how Kimi K3 performed against the three products that launched closest to it on the same day.
Who hunted Kimi K3?
Kimi K3 was hunted by Justin Jincaid. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Kimi K3 including community comment highlights and product details, visit the product overview.
Justin Jincaid
Hi everyone! 👋
While most of the industry is focused on scaling compute, Moonshot is focused on scaling intelligence.
Instead of just scaling up model parameters (which they did anyway—hitting a massive 2.8T parameters!), Kimi K3 introduces a 2.5x improvement in scaling efficiency using their custom Kimi Delta Attention and Attention Residuals architectures.
It is the world’s first open 3T-class model, and its long-horizon agentic workflows are wild:
🤯 1M Context + Native Vision: Built for massive data ingestion, from video and screens to complex systems.
💻 Autonomous Engineering: It built its own GPU compiler (MiniTriton) and optimized complex GPU kernels competitively with the strongest proprietary models.
🧠 Chip Design & Astrophysics: In a single 48-hour run, it autonomously designed and verified its own microchip. It also bridged astrophysics literature with executable code to reproduce complex stellar relations.
It's impressive to see a 2.8T model with this level of long-horizon reasoning being open-sourced.
How do you see open-source weights of this scale shifting the balance with proprietary AI?