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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?
Really glad to see Kimi tackling long papers and code in one place. One thing that would make it way more useful for me: persistent project memory. Let me upload a folder of papers or a repo, then ask follow up questions days later and have Kimi still remember the context without me re uploading everything.
Been using Kimi for a while and the paper interpretation is honestly solid. One thing I'd love is a "compare two papers side by side" mode, surfacing differences in methodology and findings automatically, that would save me a ton of time when writing lit reviews.
Useful for quickly summarizing long documents, the interface feels clean and responses come back fast. Would love more control over tone in future updates.
honestly the translation feature could use real-time voice input, like you speak and it instantly translates or transcribes. would make it way more useful for meetings or lectures when typing isnt practical. just a thought
That's impressive scale. How does it perform on long-horizon coding tasks versus other open models?
honestly the paper interpretation part was pretty solid for me, kind of speeds up the whole reading process when you just need the gist.
Finally tried Kimi for breaking down a dense research paper and it actually pulled out the methodology cleanly in like 10 seconds, saved me a real headache honestly.
Uploaded a dense methodology section from a paper and it gave me a clean summary in seconds, way better than skimming for ten minutes. Going to keep using it for my lit reviews.
scaling efficiency claim is the interesting part honestly, the chip design/compiler stuff feels more like a flex until someone outside moonshot replicates it.
vision + 1M context is nice till the compute bill shows up, anyone tested actual latency yet.
used kimi for parsing research papers before, wasn't bad. hoping 1M context makes long ones less painful.
nothing about quantized versions in the post, kinda need that info before i can even consider self hosting.
I'm really impressed by its front-end coding capabilities.
Hope it gets open-sourced soon!
"autonomously verified its own chip design" in 48hrs sounds cool but who checked the model's work here.
Tried to put K3 through a real test before commenting instead of just reading the benchmarks. Signed in, picked K3 Max from the model list, and asked it a nested Navigator Hero animation question straight from my Flutter client work. Two attempts, both came back with Task paused due to system peak. Honestly that says more about launch day demand than about the model, but I did not get my answer yet.
Two things worth flagging for the team. The composer still defaults to K2.6 Fast for signed-in users, so a lot of people arriving from this page and typing straight into the box are probably testing the old model without realizing it. And when K3 pauses under load it would be great to see queue position or an ETA instead of a bare retry link.
The open weights angle is the genuinely exciting part for me as an agency dev. A 1M context window plus native vision at open 3T class scale changes what small teams can even consider self-hosting. Congrats on shipping, upvoted, and I will retry the Flutter question once the servers cool down.
About Kimi K3 on Product Hunt
“The world's first open 3T-class model”
Kimi K3 was submitted on Product Hunt and earned 53 upvotes and 20 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.
Kimi K3 was featured in Open Source (68.6k followers), Artificial Intelligence (473.8k followers) and Development (6k followers) on Product Hunt. Together, these topics include over 124.3k products, making this a competitive space to launch in.
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.
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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?