I've only started using Claude, Gemini, etc in the last few months (I guess it comes with age, I'm no longer interested in trying the latest "tech"). I assume those are "non-agentic" models.
From reading articles online, "agentic" means like you have a "virtual" Virtual Assistant with "hands" that can google, open apps, etc, on their own.
Why not use existing "non-agentic" model and "orchestrate" them using LangChain, MCP etc? Why create a new breed of model?
I'm sorry if my questions sound silly. Following AI world is like following JavaScript world.
For what it's worth, I think Kimi's modified MIT license still meets the OSI definition of "open source." For example, the explicitly OSI-approved "Attribute Assurance License"[1] contains similar wording:
> each time the resulting executable program or a program dependent thereon is launched, a prominent display (e.g., splash screen or banner text) of the Author’s attribution information
If the SWE Bench results are to be believed... this looks best in class right now for a local LLM. To be fair, show me the guy who is running this locally...
How well separated are experts per domain in a model like that? Specifically, if I'm interested in a programming use only, could we possibly strip it to one or two of them? Or should I assume a much wider spread? (And there would be some overlap anyway from the original root model)
This is a very impressive general purpose LLM (GPT 4o, DeepSeek-V3 family). It’s also open source.
I think it hasn’t received much attention because the frontier shifted to reasoning and multi-modal AI models. In accuracy benchmarks, all the top models are reasoning ones:
This is the model release that made Sam Altman go "Oh wait actually we can't release the new open source model this week, sorry. Something something security concerns".
Perhaps their open source model release doesn't look so good compared to this one
I tried Kimi on a few coding problems that Claude was spinning on. It’s good. It’s huge, way too big to be a “local” model — I think you need something like 16 H200s to run it - but it has a slightly different vibe than some of the other models. I liked it. It would definitely be useful in ensemble use cases at the very least.
I like new, solid non-reasoning models that push the frontier. These still have nice use cases (basically anything where logic puzzles or STEM subjects don't apply) where you don't want to spend cash on reasoning tokens.
This is not open source, they have a "modified MIT license" where they have other restrictions on users over a certain threshold.
Our only modification part is that, if the Software (or any derivative works
thereof) is used for any of your commercial products or services that have
more than 100 million monthly active users, or more than 20 million US dollars
(or equivalent in other currencies) in monthly revenue, you shall prominently
display "Kimi K2" on the user interface of such product or service.
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[ 4.4 ms ] story [ 78.8 ms ] threadIs this the largest open-weight model?
From reading articles online, "agentic" means like you have a "virtual" Virtual Assistant with "hands" that can google, open apps, etc, on their own.
Why not use existing "non-agentic" model and "orchestrate" them using LangChain, MCP etc? Why create a new breed of model?
I'm sorry if my questions sound silly. Following AI world is like following JavaScript world.
> each time the resulting executable program or a program dependent thereon is launched, a prominent display (e.g., splash screen or banner text) of the Author’s attribution information
[1] https://opensource.org/license/attribution-php
Is there any way that I could do so?
Open Router? Or does kimi have their own website? Just curious to really try it out!
I think it hasn’t received much attention because the frontier shifted to reasoning and multi-modal AI models. In accuracy benchmarks, all the top models are reasoning ones:
https://artificialanalysis.ai/
If someone took Kimi k2 and trained a reasoning model with it, I’d be curious how that model performs.
Perhaps their open source model release doesn't look so good compared to this one
Open-weight. As usual, you don't get the dataset, training scripts, etc.
It's open-weight. As usual, you don't get the dataset, training scripts, etc.