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 K2.5" on the user interface of such product or service.
One. Trillion. Even on native int4 that’s… half a terabyte of vram?!
Technical awe at this marvel aside that cracks the 50th percentile of HLE, the snarky part of me says there’s only half the danger in giving something away nobody can run at home anyway…
The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/
The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds).
The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second).
At the high end, there are servers with multiple GPUs with lots of VRAM or multiple chained Macs or Strix Halo mini PCs.
The key enabler here is that the models are MoE (Mixture of Experts), which means that only a small(ish) part of the model is required to compute the next token. In this case, there are 32B active parameters, which is about 16GB at 4 bit per parameter. This only leaves the question of how to get those 16GB to the processor as fast as possible.
> The model absolutely can be run at home. There even is a big community around running large models locally
IMO 1tln parameters and 32bln active seems like a different scale to what most are talking about when they say localLLMs IMO. Totally agree there will be people messing with this, but the real value in localLLMs is that you can actually use them and get value from them with standard consumer hardware. I don't think that's really possible with this model.
32B active is nothing special, there's local setups that will easily support that. 1T total parameters ultimately requires keeping the bulk of them on SSD. This need not be an issue if there's enough locality in expert choice for any given workload; the "hot" experts will simply be cached in available spare RAM.
Which conveniently fits on one 8xH100 machine. With 100-200 GB left over for overhead, kv-cache, etc.
that's what intelligence takes. Most of intelligence is just compute
Hey have they open sourced all Kimi k2.5 (thinking,instruct,agent,agent swarm [beta])?
Because I feel like they mentioned that agent swarm is available their api and that made me feel as if it wasn't open (weights)*? Please let me know if all are open source or not?
I'm assuming the swarm part is all harness. Well I mean a harness and way of thinking that the weights have just been fine tuned to use.
> or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2.5" on the user interface of such product or service.
Why not just say "you shall pay us 1 million dollars"?
? They prefer the branding. The license just says you have to say it was them if you make > $250mm a year on the model.
Companies with $20M revenue will not normally have spare $1M available. They'd get more money by charging reasonable subscriptions than by using lawyers to chase sudden company-ending fees.
it's monthly :) $240M revenue companies will absolutely find a way to fork $1M if they need to. Kimi most likely sees the eyeballs of free advertising as more profitable in the grander scheme of things
I assume this allows them to sue for different amounts. And not discourage too many people from using it.
The "Deepseek moment" is just one year ago today!
Coincidence or not, let's just marvel for a second over this amount of magic/technology that's being given away for free... and how liberating and different this is than OpenAI and others that were closed to "protect us all".
What amazes me is why would someone spend millions to train this model and give it away for free. What is the business here?
Chinese state that maybe sees open collaboration as the way to nullify any US lead in the field, concurrently if the next "search-winner" is built upon their model the Chinese worldview that Taiwan belongs to China and Tiamen Square massacre never happened.
Also their license says that if you have a big product you need to promote them, remember how Google "gave away" site searche widgets and that was perhaps one of the major ways they gained recognition for being the search leader.
OpenAI/NVidia is the Pets.com/Sun of our generation, insane valuations, stupid spend, expensive options, expensive hardware and so on.
Sun hardware bought for 50k USD to run websites in 2000 are less capable than perhaps 5 dollar/month VPS's today?
"Scaling to AGI/ASI" was always a fools errand, best case OpenAI should've squirreled away money to have a solid engineering department that could focus on algorithmic innovations but considering that Antrophic, Google and Chinese firms have caught up or surpassed them it seems they didn't.
Once things blows up, those closed options that had somewhat sane/solid model research that handles things better will be left and a ton of new competitors running modern/cheaper hardware and just using models are building blocks.
> "Scaling to AGI/ASI" was always a fools errand
Scaling depends on hardware, so cheaper hardware on a compute-per-watt basis only makes scaling easier. There is no clear definition of AGI/ASI but AI has already scaled to be quite useful.
Speculating: there are two connected businesses here, creating the models, and serving the models. Outside of a few moneyed outliers, no one is going to run this at home. So at worst opening this model allows mid-sized competitors to serve it to customers from their own infra -- which helps Kimi gain mindshare, particularly against the large incumbents who are definitely not going to be serving Kimi and so don't benefit from its openness.
Given the shallowness of moats in the LLM market, optimizing for mindshare would not be the worst move.
I think this fits into some "Commoditize The Complement" strategy.
Curious to hear what “OpenAI” thinks the answer to this is
Hosting the model is cheaper per token, the more batched token you get. So they have big advantage here.
It’s not coincidence. Chinese companies tend to do big releases before Chinese new year. So expect more to come before Feb 17.
> For complex tasks, Kimi K2.5 can self-direct an agent swarm with up to 100 sub-agents, executing parallel workflows across up to 1,500 tool calls.
> K2.5 Agent Swarm improves performance on complex tasks through parallel, specialized execution [..] leads to an 80% reduction in end-to-end runtime
Not just RL on tool calling, but RL on agent orchestration, neat!
> Kimi K2.5 can self-direct an agent swarm
Is this within the model? Or within the IDE/service that runs the model?
Because tool calling is mostly just the agent outputting "call tool X", and the IDE does it and returns the data back to AI's context
An LLM model only outputs tokens, so this could be seen as an extension of tool calling where it has trained on the knowledge and use-cases for "tool-calling" itself as a sub-agent.
Ok, so agent swarm = tool calling where the tool is a LLM call and the argument is the prompt
Parallel agents are such a simple, yet powerful hack. Using it in Claude Code with TeammateTool and getting lots of good results!
Have you all noted that the latest releases (Qwen3 max thinking, now Kimi k2.5) from Chinese companies are benching against Claude opus now and not Sonnet? They are truly catching up, almost at the same pace?
The benching is sus, it's way more important to look at real usage scenarios.
I've read several people say that Kimi K2 has a better "emotional intelligence" than other models. I'll be interested to see whether K2.5 continues or even improves on that.
Yup, I experience the same. I don't know what they do to achieve this but it gives them this edge, really curious to learn more about what makes it so good at it.
yes, though this is highly subjective - it 'feels' like that to me as well (comapred to Gemini 3, GPT 5.2, Opus 4.5).
One thing caught my eyes is that besides K2.5 model, Moonshot AI also launched Kimi Code (https://www.kimi.com/code), evolved from Kimi CLI. It is a terminal coding agent, I've been used it last month with Kimi subscription, it is capable agent with stable harness.
>Kimi Code CLI is not only a coding agent, but also a shell.
That's cool. It also has a zsh hook, allowing you to switch to agent mode wherever you're.
How does it fare against CC?
K2 0905 and K2 Thinking shortly after that have done impressively well in my personal use cases and was severely slept on. Faster, more accurate, less expensive, more flexible in terms of hosting and available months before Gemini 3 Flash, I really struggle to understand why Flash got such positive attention at launch.
Interested in the dedicated Agent and Agent Swarm releases, especially in how that could affect third party hosting of the models.
K2 thinking didn't have vision which was a big drawback for my projects.
Curious what would be the most minimal reasonable hardware one would need to deploy this locally?
I parsed "reasonable" as in having reasonable speed to actually use this as intended (in agentic setups). In that case, it's a minimum of 70-100k for hardware (8x 6000 PRO + all the other pieces to make it work). The model comes with native INT4 quant, so ~600GB for the weights alone. An 8x 96GB setup would give you ~160GB for kv caching.
You can of course "run" this on cheaper hardware, but the speeds will not be suitable for actual use (i.e. minutes for a simple prompt, tens of minutes for high context sessions per turn).
Models of this size can usually be run using MLX on a pair of 512GB Mac Studio M3 Ultras, which are about $10,000 each so $20,000 for the pair.
I think you can put a bunch of apple silicon macs with enough ram together
e.g. in an office or coworking space
800-1000 gb ram perhaps?
A realistic setup for this would be a 16× H100 80GB with NVLink. That comfortably handles the active 32B experts plus KV cache without extreme quantization. Cost-wise we are looking at roughly $500k–$700k upfront or $40–60/hr on-demand, which makes it clear this model is aimed at serious infra teams, not casual single-GPU deployments. I’m curious how API providers will price tokens on top of that hardware reality.
You don't need to wait and see, Kimi K2 has the same hardware requirements and has several providers on OpenRouter:
Generally it seems to be in the neighborhood of $0.50/1M for input and $2.50/1M for output
The weights are int4, so you'd only need 8xH100
Generally speaking, 8xH200s will be a lot cheaper than 16xH100s, and faster too. But both should technically work.
The other realistic setup is $20k, for a small company that needs a private AI for coding or other internal agentic use with two Mac Studios connected over thunderbolt 5 RMDA.
That won’t realistically work for this model. Even with only ~32B active params, a 1T-scale MoE still needs the full expert set available for fast routing, which means hundreds of GB to TBs of weights resident. Mac Studios don’t share unified memory across machines, Thunderbolt isn’t remotely comparable to NVLink for expert exchange, and bandwidth becomes the bottleneck immediately. You could maybe load fragments experimentally, but inference would be impractically slow and brittle. It’s a very different class of workload than private coding models.
RDMA over Thunderbolt is a thing now.
People are running the previous Kimi K2 on 2 Mac Studios at 21tokens/s or 4 Macs at 30tokens/s. Its still premature, but not a completely crazy proposition for the near future, giving the rate of progress.
> 2 Mac Studios at 21tokens/s or 4 Macs at 30tokens/s
Keep in mind that most people posting speed benchmarks try them with basically 0 context. Those speeds will not hold at 32/64/128k context length.
If "fast" routing is per-token, the experts can just reside on SSD's. the performance is good enough these days. You don't need to globally share unified memory across the nodes, you'd just run distributed inference.
Anyway, in the future your local model setups will just be downloading experts on the fly from experts-exchange. That site will become as important to AI as downloadmoreram.com.
Depends on if you are using tensor parallelism or pipeline parallelism, in the second case you don't need any sharing.
I'd love to see the prompt processing speed difference between 16× H100 and 2× Mac Studio.
Prompt processing/prefill can even get some speedup from local NPU use most likely: when you're ultimately limited by thermal/power limit throttling, having more efficient compute available means more headroom.
I asked GPT for a rough estimate to benchmark prompt prefill on an 8,192 token input.
• 16× H100: 8,192 / (20k to 80k tokens/sec) ≈ 0.10 to 0.41s
• 2× Mac Studio (M3 Max): 8,192 / (150 to 700 tokens/sec) ≈ 12 to 55s
These are order-of-magnitude numbers, but the takeaway is that multi H100 boxes are plausibly ~100× faster than workstation Macs for this class of model, especially for long-context prefill.
You do realize that's entirely made up, right?
Could be true, could be fake - the only thing we can be sure of is that it's made up with no basis in reality.
This is not how you use llms effectively, that's how you give everyone that's using them a bad name from association
That's great for affordable local use but it'll be slow: even with the proper multi-node inference setup, the thunderbolt link will be a comparative bottleneck.
Kimi was already one of the best writing models. Excited to try this one out
To me, Kimi has been the best with writing and conversing, its way more human like!
Congratulations, great work Kimi team.
Why is that Claude still at the top in coding, are they heavily focused on training for coding or is it their general training is so good that it performs well in coding?
Someone please beat the Opus 4.5 in coding, I want to replace it.
I replaced Opus with Gemini Pro and it's just plain a better coder IMO. It'll restructure code to enable support for new requirements where Opus seems to just pile on more indirection layers by default, when it doesn't outright hardcode special cases inside existing functions, or drop the cases it's failing to support from the requirements while smugly informing you you don't need that anyway.
Opus 4.5 only came out two months ago, and yes Anthropic spends a lot of effort making it particularly good at coding.
I don't get this "agent swarm" concept. You set up a task and they boot up 100 LLMs to try to do it in parallel, and then one "LLM judge" puts it all together? Is there anywhere I can read more about it?
You can read about this basically everywhere - the term of art is agent orchestration. Gas town, Claude’s secret swarm mode, or people who like to use phrases like “Wiggum loop” will get you there.
If you’re really lazy - the quick summary is that you can benefit from the sweet spot of context length and reduce instruction overload while getting some parallelism benefits from farming tasks out to LLMs with different instructions. The way this is generally implemented today is through tool calling, although Claude also has a skills interface it has been trained against.
So the idea would be for software development, why not have a project/product manager spin out tasks to a bunch of agents that are primed to be good at different things? E.g. an architect, a designer, and so on. Then you just need something that can rectify GitHub PRs and bob’s your uncle.
Gas town takes a different approach and parallelizes on coding tasks of any sort at the base layer, and uses the orchestration infrastructure to keep those coders working constantly, optimizing for minimal human input.
I'm not sure whether there are parts of this done for claude but those other ones are layers on top of the usual LLMs we see. This seems to be a bit different, in that there's a different model trained specifically for splitting up and managing the workload.
I've also been quite skeptical, and I became even more skeptical after hearing a tech talk from a startup in this space [1].
I think the best way to think about it is that its an engineering hack to deal with a shortcoming of LLMs: for complex queries LLMs are unable to directly compute a SOLUTION given a PROMPT, but are instead able to break down the prompt to intermediate solutions and eventually solve the original prompt. These "orchestrator" / "swarm" agents add some formalism to this and allow you to distribute compute, and then also use specialized models for some of the sub problems.
You have a team lead that establishes a list of tasks that are needed to achieve your mission
then it creates a list of employees, each of them is specialized for a task, and they work in parallel.
Essentially hiring a team of people who get specialized on one problem.
Do one thing and do it well.
But in the end, isn't this the same idea with the MoE?
Where we have more specialized "jobs", which the model is actually trained for.
I think the main difference with agents swarm is the ability to run them in parallel. I don't see how this adds much compared to simply sending multiple API calls in parallel with your desired tasks. I guess the only difference is that you let the AI decide how to split those requests and what each task should be.
Nope. MoE is strictly about model parameter sparsity. Agents are about running multiple small-scale tasks in parallel and aggregating the results for further processing - it saves a lot of context length compared to having it all in a single session, and context length has quadratic compute overhead so this matters. You can have both.
One positive side effect of this is that if subagent tasks can be dispatched to cheaper and more efficient edge-inference hardware that can be deployed at scale (think nVidia Jetsons or even Apple Macs or AMD APU's) even though it might be highly limited in what can fit on the single node, then complex coding tasks ultimately become a lot cheaper per token than generic chat.
Yes, I know you can have both.
My point was that this is just a different way of creating specialised task solvers, the same as with MoE.
And, as you said, with MoE it's about the model itself, and it's done at training level so that's not something we can easily do ourselves.
But with agent swarm, isn't it simply splitting a task in multiple sub-tasks and sending each one in a different API call? So this can be done with any of the previous models too, only that the user has to manually define those tasks/contexts for each query.
Or is this at a much more granular level than this, which would not be feasible to be done by hand?
I was already doing this in n8n, creating different agents with different system prompts for different tasks. I am not sure if automating this (with swarm) would work well in my most cases, I don't see how this fully complements Tools or Skills
MoE has nothing whatsoever to do with specialized task solvers. It always operates per token within a single task, you can think of it perhaps as a kind of learned "attention" for model parameters as opposed to context data.
Yes, specific weights/parameters have be trained to solve specific tasks (trained on different data).
Or did I misunderstand the concept of MoE, and it's not about having specific parts of the model (parameters) do better on specific input contexts?
doesn't work, looks like the link or SVG was cropped.
No pelican for me :(
[deleted]
As your local vision nut, their claims about "SOTA" vision are absolutely BS in my tests.
Sure it's SOTA at standard vision benchmarks. But on tasks that require proper image understanding, see for example BabyVision[0] it appears very much lacking compared to Gemini 3 Pro.
About 600GB needed for weights alone, so on AWS you need an p5.48xlarge (8× H100) which costs $55/hour.
Can we please stop calling those models "open source"? Yes the weights are open. So, "open weight" maybe. But the source isn't open, the thing that allows to re-create it. That's what "open source" used to mean. (Together with a license that allows you to use that source for various things.)
Glad to to see open source models are catching up and treat vision as first-class citizen (a.k.a native multimodal agentic model). GLM and Qwen models takes different approach, by having a base model and a vision variant (glm-4.6 vs glm-4.6v).
I guess after Kimi K2.5, other vendors are going to the same route?
Can't wait to see how this model performs on computer automation use cases like
VITA AI Coworker.
Is this actually good or just optimized heavily for benchmarks? I am hopefully its the former based on the writeup but need to put it through its paces.
There are so many models, is there any website with list of all of them and comparison of performance on different tasks?
The post actually has great benchmark tables inside of it. They might be outdated in a few months, but for now, it gives you a great summary. Seems like Gemini wins on image and video perf, Claude is the best at coding, ChatGPT is the best for general knowledge.
But ultimately, you need to try them yourself on the tasks you care about and just see. My personal experience is that right now, Gemini Pro performs the best at everything I throw at it. I think it's superior to Claude and all of the OSS models by a small margin, even for things like coding.
I like Gemini Pro's UI over Claude so much but honestly I might start using Kimi K2.5 if its open source & just +/- Gemini Pro/Chatgpt/Claude because at that point I feel like the results are negligible and we are getting SOTA open source models again.
> honestly I might start using Kimi K2.5 if its open source & just +/- Gemini Pro/Chatgpt/Claude because at that point I feel like the results are negligible and we are getting SOTA open source models again.
Me too!
> I like Gemini Pro's UI over Claude so much
This I don't understand. I mean, I don't see a lot of difference in both UIs. Quite the opposite, apart from some animations, round corners and color gradings, they seem to look very alike, no?
Y'know I ended up buying Kimi's moderato plan which is 19$ but they had this unique idea where you can talk to a bot and they could reduce the price
I made it reduce the price of first month to 1.49$ (It could go to 0.99$ and my frugal mind wanted it haha but I just couldn't have it do that lol)
Anyways, afterwards for privacy purposes/( I am a minor so don't have a card), ended up going to g2a to get a 10$ Visa gift card essentially and used it. (I had to pay a 1$ extra but sure)
Installed kimi code on my mac and trying it out. Honestly, I am kind of liking it.
My internal benchmark is creating pomodoro apps in golang web... Gemini 3 pro has nailed it, I just tried the kimi version and it does have some bugs but it feels like it added more features.
Gonna have to try it out for a month.
I mean I just wish it was this cheap for the whole year :< (As I could then move from, say using the completely free models)
There are many lists, but I find all of them outdated or containing wrong information or missing the actual benchmarks I'm looking for.
I was thinking, that maybe it's better to make my own benchmarks with the questions/things I'm interested in, and whenever a new model comes out run those tests with that model using open-router.
Thank you! Exactly what I was looking for
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Those are some impressive benchmark results. I wonder how well it does in real life.
Maybe we can get away with something cheaper than Claude for coding.
I'm curious about the "cheaper" claim -- I checked Kimi pricing, and it's a $200/mo subscription too?
On openrouter 2.5 is at 0.60/3$ per Mtok. That's haiku pricing.
The unit economics seem tough at that price for a 1T parameter model. Even with MoE sparsity you are still VRAM bound just keeping the weights resident, which is a much higher baseline cost than serving a smaller model like Haiku.
They also have a $20 and $40 tier.
If you bargain with their bot Kimmmmy (not joking), you can even get lower pricing.
they cooked
[dead]
Actually open source, or yet another public model, which is the equivalent of a binary?
URL is down so cannot tell.
The label 'open source' has become a reputation reaping and marketing vehicle rather than an informative term since the Hugging Face benchmark race started. With the weights only, we cannot actually audit that if a model is a) contaminated by benchmarks, b) built with deliberate biases, or c) trained on copyrighted/privacy data, let alone allowing other vendors to replicate the results. Anyways, people still love free stuff.
Just accept that IP laws don't matter and the old "free software" paradigm is dead. Aaron Swartz died so that GenAI may live. RMS and his model of "copyleft" are so Web 1.0 (not even 2.0). No one in GenAI cares AT ALL about the true definition of open source. Good.
Huggingface Link: https://huggingface.co/moonshotai/Kimi-K2.5
1T parameters, 32b active parameters.
License: MIT with the following modification:
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 K2.5" on the user interface of such product or service.
One. Trillion. Even on native int4 that’s… half a terabyte of vram?!
Technical awe at this marvel aside that cracks the 50th percentile of HLE, the snarky part of me says there’s only half the danger in giving something away nobody can run at home anyway…
The model absolutely can be run at home. There even is a big community around running large models locally: https://www.reddit.com/r/LocalLLaMA/
The cheapest way is to stream it from a fast SSD, but it will be quite slow (one token every few seconds).
The next step up is an old server with lots of RAM and many memory channels with maybe a GPU thrown in for faster prompt processing (low two digits tokens/second).
At the high end, there are servers with multiple GPUs with lots of VRAM or multiple chained Macs or Strix Halo mini PCs.
The key enabler here is that the models are MoE (Mixture of Experts), which means that only a small(ish) part of the model is required to compute the next token. In this case, there are 32B active parameters, which is about 16GB at 4 bit per parameter. This only leaves the question of how to get those 16GB to the processor as fast as possible.
> The model absolutely can be run at home. There even is a big community around running large models locally
IMO 1tln parameters and 32bln active seems like a different scale to what most are talking about when they say localLLMs IMO. Totally agree there will be people messing with this, but the real value in localLLMs is that you can actually use them and get value from them with standard consumer hardware. I don't think that's really possible with this model.
32B active is nothing special, there's local setups that will easily support that. 1T total parameters ultimately requires keeping the bulk of them on SSD. This need not be an issue if there's enough locality in expert choice for any given workload; the "hot" experts will simply be cached in available spare RAM.
Which conveniently fits on one 8xH100 machine. With 100-200 GB left over for overhead, kv-cache, etc.
that's what intelligence takes. Most of intelligence is just compute
Hey have they open sourced all Kimi k2.5 (thinking,instruct,agent,agent swarm [beta])?
Because I feel like they mentioned that agent swarm is available their api and that made me feel as if it wasn't open (weights)*? Please let me know if all are open source or not?
I'm assuming the swarm part is all harness. Well I mean a harness and way of thinking that the weights have just been fine tuned to use.
> or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2.5" on the user interface of such product or service.
Why not just say "you shall pay us 1 million dollars"?
? They prefer the branding. The license just says you have to say it was them if you make > $250mm a year on the model.
Companies with $20M revenue will not normally have spare $1M available. They'd get more money by charging reasonable subscriptions than by using lawyers to chase sudden company-ending fees.
it's monthly :) $240M revenue companies will absolutely find a way to fork $1M if they need to. Kimi most likely sees the eyeballs of free advertising as more profitable in the grander scheme of things
I assume this allows them to sue for different amounts. And not discourage too many people from using it.
The "Deepseek moment" is just one year ago today!
Coincidence or not, let's just marvel for a second over this amount of magic/technology that's being given away for free... and how liberating and different this is than OpenAI and others that were closed to "protect us all".
What amazes me is why would someone spend millions to train this model and give it away for free. What is the business here?
Chinese state that maybe sees open collaboration as the way to nullify any US lead in the field, concurrently if the next "search-winner" is built upon their model the Chinese worldview that Taiwan belongs to China and Tiamen Square massacre never happened.
Also their license says that if you have a big product you need to promote them, remember how Google "gave away" site searche widgets and that was perhaps one of the major ways they gained recognition for being the search leader.
OpenAI/NVidia is the Pets.com/Sun of our generation, insane valuations, stupid spend, expensive options, expensive hardware and so on.
Sun hardware bought for 50k USD to run websites in 2000 are less capable than perhaps 5 dollar/month VPS's today?
"Scaling to AGI/ASI" was always a fools errand, best case OpenAI should've squirreled away money to have a solid engineering department that could focus on algorithmic innovations but considering that Antrophic, Google and Chinese firms have caught up or surpassed them it seems they didn't.
Once things blows up, those closed options that had somewhat sane/solid model research that handles things better will be left and a ton of new competitors running modern/cheaper hardware and just using models are building blocks.
> "Scaling to AGI/ASI" was always a fools errand
Scaling depends on hardware, so cheaper hardware on a compute-per-watt basis only makes scaling easier. There is no clear definition of AGI/ASI but AI has already scaled to be quite useful.
Speculating: there are two connected businesses here, creating the models, and serving the models. Outside of a few moneyed outliers, no one is going to run this at home. So at worst opening this model allows mid-sized competitors to serve it to customers from their own infra -- which helps Kimi gain mindshare, particularly against the large incumbents who are definitely not going to be serving Kimi and so don't benefit from its openness.
Given the shallowness of moats in the LLM market, optimizing for mindshare would not be the worst move.
I think this fits into some "Commoditize The Complement" strategy.
https://gwern.net/complement
Curious to hear what “OpenAI” thinks the answer to this is
Hosting the model is cheaper per token, the more batched token you get. So they have big advantage here.
It’s not coincidence. Chinese companies tend to do big releases before Chinese new year. So expect more to come before Feb 17.
> For complex tasks, Kimi K2.5 can self-direct an agent swarm with up to 100 sub-agents, executing parallel workflows across up to 1,500 tool calls.
> K2.5 Agent Swarm improves performance on complex tasks through parallel, specialized execution [..] leads to an 80% reduction in end-to-end runtime
Not just RL on tool calling, but RL on agent orchestration, neat!
> Kimi K2.5 can self-direct an agent swarm
Is this within the model? Or within the IDE/service that runs the model?
Because tool calling is mostly just the agent outputting "call tool X", and the IDE does it and returns the data back to AI's context
An LLM model only outputs tokens, so this could be seen as an extension of tool calling where it has trained on the knowledge and use-cases for "tool-calling" itself as a sub-agent.
Ok, so agent swarm = tool calling where the tool is a LLM call and the argument is the prompt
Parallel agents are such a simple, yet powerful hack. Using it in Claude Code with TeammateTool and getting lots of good results!
> TeammateTool
What is this?
https://x.com/kieranklaassen/status/2014830266515382693 - agent swarms tool shipping w/ cc soon..
claude code hidden feaure currently under a feature flag:
https://github.com/mikekelly/claude-sneakpeek
Have you all noted that the latest releases (Qwen3 max thinking, now Kimi k2.5) from Chinese companies are benching against Claude opus now and not Sonnet? They are truly catching up, almost at the same pace?
The benching is sus, it's way more important to look at real usage scenarios.
I've read several people say that Kimi K2 has a better "emotional intelligence" than other models. I'll be interested to see whether K2.5 continues or even improves on that.
Yup, I experience the same. I don't know what they do to achieve this but it gives them this edge, really curious to learn more about what makes it so good at it.
yes, though this is highly subjective - it 'feels' like that to me as well (comapred to Gemini 3, GPT 5.2, Opus 4.5).
One thing caught my eyes is that besides K2.5 model, Moonshot AI also launched Kimi Code (https://www.kimi.com/code), evolved from Kimi CLI. It is a terminal coding agent, I've been used it last month with Kimi subscription, it is capable agent with stable harness.
GitHub: https://github.com/MoonshotAI/kimi-cli
>Kimi Code CLI is not only a coding agent, but also a shell.
That's cool. It also has a zsh hook, allowing you to switch to agent mode wherever you're.
How does it fare against CC?
K2 0905 and K2 Thinking shortly after that have done impressively well in my personal use cases and was severely slept on. Faster, more accurate, less expensive, more flexible in terms of hosting and available months before Gemini 3 Flash, I really struggle to understand why Flash got such positive attention at launch.
Interested in the dedicated Agent and Agent Swarm releases, especially in how that could affect third party hosting of the models.
K2 thinking didn't have vision which was a big drawback for my projects.
Curious what would be the most minimal reasonable hardware one would need to deploy this locally?
I parsed "reasonable" as in having reasonable speed to actually use this as intended (in agentic setups). In that case, it's a minimum of 70-100k for hardware (8x 6000 PRO + all the other pieces to make it work). The model comes with native INT4 quant, so ~600GB for the weights alone. An 8x 96GB setup would give you ~160GB for kv caching.
You can of course "run" this on cheaper hardware, but the speeds will not be suitable for actual use (i.e. minutes for a simple prompt, tens of minutes for high context sessions per turn).
Models of this size can usually be run using MLX on a pair of 512GB Mac Studio M3 Ultras, which are about $10,000 each so $20,000 for the pair.
I think you can put a bunch of apple silicon macs with enough ram together
e.g. in an office or coworking space
800-1000 gb ram perhaps?
A realistic setup for this would be a 16× H100 80GB with NVLink. That comfortably handles the active 32B experts plus KV cache without extreme quantization. Cost-wise we are looking at roughly $500k–$700k upfront or $40–60/hr on-demand, which makes it clear this model is aimed at serious infra teams, not casual single-GPU deployments. I’m curious how API providers will price tokens on top of that hardware reality.
You don't need to wait and see, Kimi K2 has the same hardware requirements and has several providers on OpenRouter:
https://openrouter.ai/moonshotai/kimi-k2-thinking https://openrouter.ai/moonshotai/kimi-k2-0905 https://openrouter.ai/moonshotai/kimi-k2-0905:exacto https://openrouter.ai/moonshotai/kimi-k2
Generally it seems to be in the neighborhood of $0.50/1M for input and $2.50/1M for output
The weights are int4, so you'd only need 8xH100
Generally speaking, 8xH200s will be a lot cheaper than 16xH100s, and faster too. But both should technically work.
The other realistic setup is $20k, for a small company that needs a private AI for coding or other internal agentic use with two Mac Studios connected over thunderbolt 5 RMDA.
That won’t realistically work for this model. Even with only ~32B active params, a 1T-scale MoE still needs the full expert set available for fast routing, which means hundreds of GB to TBs of weights resident. Mac Studios don’t share unified memory across machines, Thunderbolt isn’t remotely comparable to NVLink for expert exchange, and bandwidth becomes the bottleneck immediately. You could maybe load fragments experimentally, but inference would be impractically slow and brittle. It’s a very different class of workload than private coding models.
RDMA over Thunderbolt is a thing now.
People are running the previous Kimi K2 on 2 Mac Studios at 21tokens/s or 4 Macs at 30tokens/s. Its still premature, but not a completely crazy proposition for the near future, giving the rate of progress.
> 2 Mac Studios at 21tokens/s or 4 Macs at 30tokens/s
Keep in mind that most people posting speed benchmarks try them with basically 0 context. Those speeds will not hold at 32/64/128k context length.
If "fast" routing is per-token, the experts can just reside on SSD's. the performance is good enough these days. You don't need to globally share unified memory across the nodes, you'd just run distributed inference.
Anyway, in the future your local model setups will just be downloading experts on the fly from experts-exchange. That site will become as important to AI as downloadmoreram.com.
Depends on if you are using tensor parallelism or pipeline parallelism, in the second case you don't need any sharing.
I'd love to see the prompt processing speed difference between 16× H100 and 2× Mac Studio.
Prompt processing/prefill can even get some speedup from local NPU use most likely: when you're ultimately limited by thermal/power limit throttling, having more efficient compute available means more headroom.
I asked GPT for a rough estimate to benchmark prompt prefill on an 8,192 token input. • 16× H100: 8,192 / (20k to 80k tokens/sec) ≈ 0.10 to 0.41s • 2× Mac Studio (M3 Max): 8,192 / (150 to 700 tokens/sec) ≈ 12 to 55s
These are order-of-magnitude numbers, but the takeaway is that multi H100 boxes are plausibly ~100× faster than workstation Macs for this class of model, especially for long-context prefill.
You do realize that's entirely made up, right?
Could be true, could be fake - the only thing we can be sure of is that it's made up with no basis in reality.
This is not how you use llms effectively, that's how you give everyone that's using them a bad name from association
That's great for affordable local use but it'll be slow: even with the proper multi-node inference setup, the thunderbolt link will be a comparative bottleneck.
Kimi was already one of the best writing models. Excited to try this one out
To me, Kimi has been the best with writing and conversing, its way more human like!
Congratulations, great work Kimi team.
Why is that Claude still at the top in coding, are they heavily focused on training for coding or is it their general training is so good that it performs well in coding?
Someone please beat the Opus 4.5 in coding, I want to replace it.
I replaced Opus with Gemini Pro and it's just plain a better coder IMO. It'll restructure code to enable support for new requirements where Opus seems to just pile on more indirection layers by default, when it doesn't outright hardcode special cases inside existing functions, or drop the cases it's failing to support from the requirements while smugly informing you you don't need that anyway.
Opus 4.5 only came out two months ago, and yes Anthropic spends a lot of effort making it particularly good at coding.
I don't get this "agent swarm" concept. You set up a task and they boot up 100 LLMs to try to do it in parallel, and then one "LLM judge" puts it all together? Is there anywhere I can read more about it?
You can read about this basically everywhere - the term of art is agent orchestration. Gas town, Claude’s secret swarm mode, or people who like to use phrases like “Wiggum loop” will get you there.
If you’re really lazy - the quick summary is that you can benefit from the sweet spot of context length and reduce instruction overload while getting some parallelism benefits from farming tasks out to LLMs with different instructions. The way this is generally implemented today is through tool calling, although Claude also has a skills interface it has been trained against.
So the idea would be for software development, why not have a project/product manager spin out tasks to a bunch of agents that are primed to be good at different things? E.g. an architect, a designer, and so on. Then you just need something that can rectify GitHub PRs and bob’s your uncle.
Gas town takes a different approach and parallelizes on coding tasks of any sort at the base layer, and uses the orchestration infrastructure to keep those coders working constantly, optimizing for minimal human input.
I'm not sure whether there are parts of this done for claude but those other ones are layers on top of the usual LLMs we see. This seems to be a bit different, in that there's a different model trained specifically for splitting up and managing the workload.
I've also been quite skeptical, and I became even more skeptical after hearing a tech talk from a startup in this space [1].
I think the best way to think about it is that its an engineering hack to deal with a shortcoming of LLMs: for complex queries LLMs are unable to directly compute a SOLUTION given a PROMPT, but are instead able to break down the prompt to intermediate solutions and eventually solve the original prompt. These "orchestrator" / "swarm" agents add some formalism to this and allow you to distribute compute, and then also use specialized models for some of the sub problems.
[1] https://www.deepflow.com/
You have a team lead that establishes a list of tasks that are needed to achieve your mission
then it creates a list of employees, each of them is specialized for a task, and they work in parallel.
Essentially hiring a team of people who get specialized on one problem.
Do one thing and do it well.
But in the end, isn't this the same idea with the MoE?
Where we have more specialized "jobs", which the model is actually trained for.
I think the main difference with agents swarm is the ability to run them in parallel. I don't see how this adds much compared to simply sending multiple API calls in parallel with your desired tasks. I guess the only difference is that you let the AI decide how to split those requests and what each task should be.
Nope. MoE is strictly about model parameter sparsity. Agents are about running multiple small-scale tasks in parallel and aggregating the results for further processing - it saves a lot of context length compared to having it all in a single session, and context length has quadratic compute overhead so this matters. You can have both.
One positive side effect of this is that if subagent tasks can be dispatched to cheaper and more efficient edge-inference hardware that can be deployed at scale (think nVidia Jetsons or even Apple Macs or AMD APU's) even though it might be highly limited in what can fit on the single node, then complex coding tasks ultimately become a lot cheaper per token than generic chat.
Yes, I know you can have both.
My point was that this is just a different way of creating specialised task solvers, the same as with MoE.
And, as you said, with MoE it's about the model itself, and it's done at training level so that's not something we can easily do ourselves.
But with agent swarm, isn't it simply splitting a task in multiple sub-tasks and sending each one in a different API call? So this can be done with any of the previous models too, only that the user has to manually define those tasks/contexts for each query.
Or is this at a much more granular level than this, which would not be feasible to be done by hand?
I was already doing this in n8n, creating different agents with different system prompts for different tasks. I am not sure if automating this (with swarm) would work well in my most cases, I don't see how this fully complements Tools or Skills
MoE has nothing whatsoever to do with specialized task solvers. It always operates per token within a single task, you can think of it perhaps as a kind of learned "attention" for model parameters as opposed to context data.
Yes, specific weights/parameters have be trained to solve specific tasks (trained on different data).
Or did I misunderstand the concept of MoE, and it's not about having specific parts of the model (parameters) do better on specific input contexts?
The datacenters yearn for the chips.
Pretty cute pelican https://tools.simonwillison.net/svg-render#%3Csvg%20viewBox%...
doesn't work, looks like the link or SVG was cropped.
No pelican for me :(
As your local vision nut, their claims about "SOTA" vision are absolutely BS in my tests.
Sure it's SOTA at standard vision benchmarks. But on tasks that require proper image understanding, see for example BabyVision[0] it appears very much lacking compared to Gemini 3 Pro.
[0] https://arxiv.org/html/2601.06521v1
https://archive.is/P98JR
About 600GB needed for weights alone, so on AWS you need an p5.48xlarge (8× H100) which costs $55/hour.
Can we please stop calling those models "open source"? Yes the weights are open. So, "open weight" maybe. But the source isn't open, the thing that allows to re-create it. That's what "open source" used to mean. (Together with a license that allows you to use that source for various things.)
Glad to to see open source models are catching up and treat vision as first-class citizen (a.k.a native multimodal agentic model). GLM and Qwen models takes different approach, by having a base model and a vision variant (glm-4.6 vs glm-4.6v).
I guess after Kimi K2.5, other vendors are going to the same route?
Can't wait to see how this model performs on computer automation use cases like VITA AI Coworker.
https://www.vita-ai.net/
Is this actually good or just optimized heavily for benchmarks? I am hopefully its the former based on the writeup but need to put it through its paces.
There are so many models, is there any website with list of all of them and comparison of performance on different tasks?
The post actually has great benchmark tables inside of it. They might be outdated in a few months, but for now, it gives you a great summary. Seems like Gemini wins on image and video perf, Claude is the best at coding, ChatGPT is the best for general knowledge.
But ultimately, you need to try them yourself on the tasks you care about and just see. My personal experience is that right now, Gemini Pro performs the best at everything I throw at it. I think it's superior to Claude and all of the OSS models by a small margin, even for things like coding.
I like Gemini Pro's UI over Claude so much but honestly I might start using Kimi K2.5 if its open source & just +/- Gemini Pro/Chatgpt/Claude because at that point I feel like the results are negligible and we are getting SOTA open source models again.
> honestly I might start using Kimi K2.5 if its open source & just +/- Gemini Pro/Chatgpt/Claude because at that point I feel like the results are negligible and we are getting SOTA open source models again.
Me too!
> I like Gemini Pro's UI over Claude so much
This I don't understand. I mean, I don't see a lot of difference in both UIs. Quite the opposite, apart from some animations, round corners and color gradings, they seem to look very alike, no?
Y'know I ended up buying Kimi's moderato plan which is 19$ but they had this unique idea where you can talk to a bot and they could reduce the price
I made it reduce the price of first month to 1.49$ (It could go to 0.99$ and my frugal mind wanted it haha but I just couldn't have it do that lol)
Anyways, afterwards for privacy purposes/( I am a minor so don't have a card), ended up going to g2a to get a 10$ Visa gift card essentially and used it. (I had to pay a 1$ extra but sure)
Installed kimi code on my mac and trying it out. Honestly, I am kind of liking it.
My internal benchmark is creating pomodoro apps in golang web... Gemini 3 pro has nailed it, I just tried the kimi version and it does have some bugs but it feels like it added more features.
Gonna have to try it out for a month.
I mean I just wish it was this cheap for the whole year :< (As I could then move from, say using the completely free models)
Gonna have to try it out more!
There is https://artificialanalysis.ai
There are many lists, but I find all of them outdated or containing wrong information or missing the actual benchmarks I'm looking for.
I was thinking, that maybe it's better to make my own benchmarks with the questions/things I'm interested in, and whenever a new model comes out run those tests with that model using open-router.
Thank you! Exactly what I was looking for
Those are some impressive benchmark results. I wonder how well it does in real life.
Maybe we can get away with something cheaper than Claude for coding.
I'm curious about the "cheaper" claim -- I checked Kimi pricing, and it's a $200/mo subscription too?
On openrouter 2.5 is at 0.60/3$ per Mtok. That's haiku pricing.
The unit economics seem tough at that price for a 1T parameter model. Even with MoE sparsity you are still VRAM bound just keeping the weights resident, which is a much higher baseline cost than serving a smaller model like Haiku.
They also have a $20 and $40 tier.
If you bargain with their bot Kimmmmy (not joking), you can even get lower pricing.
they cooked
[dead]
Actually open source, or yet another public model, which is the equivalent of a binary?
URL is down so cannot tell.
The label 'open source' has become a reputation reaping and marketing vehicle rather than an informative term since the Hugging Face benchmark race started. With the weights only, we cannot actually audit that if a model is a) contaminated by benchmarks, b) built with deliberate biases, or c) trained on copyrighted/privacy data, let alone allowing other vendors to replicate the results. Anyways, people still love free stuff.
Just accept that IP laws don't matter and the old "free software" paradigm is dead. Aaron Swartz died so that GenAI may live. RMS and his model of "copyleft" are so Web 1.0 (not even 2.0). No one in GenAI cares AT ALL about the true definition of open source. Good.
Good?
It's open weights, not open source.
Cool
The chefs at Moonshot have cooked once again.