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…

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?

> 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"?

What amazes me is why would someone spend millions to train this model and give it away for free. What is the business here?

It’s not coincidence. Chinese companies tend to do big releases before Chinese new year. So expect more to come before Feb 17.

> 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

Parallel agents are such a simple, yet powerful hack. Using it in Claude Code with TeammateTool and getting lots of good results!

The benching is sus, it's way more important to look at real usage scenarios.

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).

>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 thinking didn't have vision which was a big drawback for my projects.

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?

To me, Kimi has been the best with writing and conversing, its way more human like!

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.

I don't think that kind of difference in benchmarks has any meaning at all. Your agentic coding tool and the task you are working on introduce a lot more "noise" than that small delta.

Also consider they are all overfitting on the benchmark itself so there might be that as well (which can go in either directions)

I consider the top models practically identical for coding applications (just personal experience with heavy use of both GPT5.2 and Opus 4.5).

Excited to see how this model compares in real applications. It's 1/5th of the price of top models!!

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.

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'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.

The datacenters yearn for the chips.

Oops, here's a working link: https://gist.github.com/simonw/32a85e337fbc6ee935d10d89726c0...

doesn't work, looks like the link or SVG was cropped.

No pelican for me :(

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.

There is https://artificialanalysis.ai

I'm curious about the "cheaper" claim -- I checked Kimi pricing, and it's a $200/mo subscription too?

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.

It's open weights, not open source.