Pretty cute pelican https://tools.simonwillison.net/svg-render#%3Csvg%20viewBox%...
No pelican for me :(
Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
101–110 of 251 posts
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#102Earlier quoted context omitted.
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…
> "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.
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#103Earlier quoted context omitted.
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…
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 che…
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
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#104Earlier quoted context omitted.
> 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
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#105A 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
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#106Earlier quoted context omitted.
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 che…
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…
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.
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#107The "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".
It’s not coincidence. Chinese companies tend to do big releases before Chinese new year. So expect more to come before Feb 17.
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#108One 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
How does it fare against CC?
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#109Earlier quoted context omitted.
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…
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?
Re: Kimi Released Kimi K2.5, Open-Source Visual SOTA-Agentic Model
#110Earlier quoted context omitted.
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 t…
> 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.