Kimi K2.5 Technical Report [pdf]
(github.com)372 points by vinhnx 2 days ago
372 points by vinhnx 2 days ago
Out of curiosity, what kind of specs do you have (GPU / RAM)? I saw the requirements and it's a beyond my budget so I am "stuck" with smaller Qwen coders.
I'm not running it locally (it's gigantic!) I'm using the API at https://platform.moonshot.ai
Just curious - how does it compare to GLM 4.7? Ever since they gave the $28/year deal, I've been using it for personal projects and am very happy with it (via opencode).
It is possible to run locally though ... I saw a video of someone running one of the heavily quantized versions on a Mac Studio, and performing pretty well in terms of speed.
I'm guessing a 256GB Mac Studio, costing $5-6K, but that wouldn't be an outrageous amount to spend for a professional tool if the model capability justified it.
> It is possible to run locally though
> running one of the heavily quantized versions
There is night and day difference in generation quality between even something like 8-bit and "heavily quantized" versions. Why not quantize to 1-bit anyway? Would that qualify as "running the model?" Food for thought. Don't get me wrong: there's plenty of stuff you can actually run on 96 GB Mac studio (let alone on 128/256 GB ones) but 1T-class models are not in that category, unfortunately. Unless you put four of them in a rack or something.
API costs on these big models over private hosts tend to be a lot less than API calls to the big 4 American platforms. You definitely get more bang for your buck.
You could run the full, unquantized model at high speed with 8 RTX 6000 Blackwell boards.
I don't see a way to put together a decent system of that scale for less than $100K, given RAM and SSD prices. A system with 4x H200s would cost more like $200K.
Did you use Kimi Code or some other harness? I used it with OpenCode and it was bumbling around through some tasks that Claude handles with ease.
Are you on the latest version? They pushed an update yesterday that greatly improved Kimi K2.5’s performance. It’s also free for a week in OpenCode, sponsored by their inference provider
I've been using it with opencode. You can either use your kimi code subscription (flat fee), moonshot.ai api key (per token) or openrouter to access it. OpenCode works beautifully with the model.
Edit: as a side note, I only installed opencode to try this model and I gotta say it is pretty good. Did not think it'd be as good as claude code but its just fine. Been using it with codex too.
I tried to use opencode for kimi k2.5 too but recently they changed their pricing from 200 tool requests/5 hour to token based pricing.
I can only speak from the tool request based but for some reason anecdotally opencode took like 10 requests in like 3-4 minutes where Kimi cli took 2-3
So I personally like/stick with the kimi cli for kimi coding. I haven't tested it out again with OpenAI with teh new token based pricing but I do think that opencode might add more token issue.
Kimi Cli's pretty good too imo. You should check it out!
Running it via https://platform.moonshot.ai -- using OpenCode. They have super cheap monthly plans at kimi.com too, but I'm not using it because I already have codex and claude monthly plans.
Where? https://www.kimi.com/code starts at $19/month, which is same as the big boys.
so there's a free plan at moonshot.ai that gives you some number of tokens without paying?
> Can you share how you're running it?
Not OP, but I've been running it through Kagi [1]. Their AI offering is probably the best-kept secret in the market.
https://unsloth.ai/docs/models/kimi-k2.5
Requirements are listed.
To save everyone a click
> The 1.8-bit (UD-TQ1_0) quant will run on a single 24GB GPU if you offload all MoE layers to system RAM (or a fast SSD). With ~256GB RAM, expect ~10 tokens/s. The full Kimi K2.5 model is 630GB and typically requires at least 4× H200 GPUs. If the model fits, you will get >40 tokens/s when using a B200. To run the model in near full precision, you can use the 4-bit or 5-bit quants. You can use any higher just to be safe. For strong performance, aim for >240GB of unified memory (or combined RAM+VRAM) to reach 10+ tokens/s. If you’re below that, it'll work but speed will drop (llama.cpp can still run via mmap/disk offload) and may fall from ~10 tokens/s to <2 token/s. We recommend UD-Q2_K_XL (375GB) as a good size/quality balance. Best rule of thumb: RAM+VRAM ≈ the quant size; otherwise it’ll still work, just slower due to offloading.
Been using K2.5 Thinking via Nano-GPT subscription and `nanocode run` and it's working quite nicely. No issues with Tool Calling so far.
I tried kimi k2.5 and first I didn't really like it. I was critical of it but then I started liking it. Also, the model has kind of replaced how I use chatgpt too & I really love kimi 2.5 the most right now (although gemini models come close too)
To be honest, I do feel like kimi k2.5 is the best open source model. It's not the best model itself right now tho but its really price performant and for many use cases might be nice depending on it.
It might not be the completely SOTA that people say but it comes pretty close and its open source and I trust the open source part because I feel like other providers can also run it and just about a lot of other things too (also considering that iirc chatgpt recently slashed some old models)
I really appreciate kimi for still open sourcing their complete SOTA and then releasing some research papers on top of them unlike Qwen which has closed source its complete SOTA.
Thank you Kimi!
Seems that K2.5 has lost a lot of the personality from K2 unfortunately, talks in more ChatGPT/Gemini/C-3PO style now. It's not explictly bad, I'm sure most people won't care but it was something that made it unique so it's a shame to see it go.
examples to illustrate
https://www.kimi.com/share/19c115d6-6402-87d5-8000-000062fec... (K2.5)
https://www.kimi.com/share/19c11615-8a92-89cb-8000-000063ee6... (K2)
K2 in your example is using the GPT reply template (tl;dr - terse details - conclusion, with contradictory tendencies), there's nothing unique about it. That's exactly how GPT-5.0 talked. The only model with a strong "personality" vibe was Claude 3 Opus.
> The only model with a strong "personality" vibe was Claude 3 Opus.
Did you have the chance to use 3.5 (or 3.6) Sonnet, and if yes, how did they compare?
As a non-paying user, 3.5 era Claude was absolutely the best LLM I've ever used in terms of having a conversation. It felt like talking to a human and not a bot. Its replies were readable, even if they were several paragraphs long. I've unfortunately never found anything remotely as good.
Pretty poorly in that regard. In 3.5 they killed Claude 3's agency, pretty much reversing their previous training policy in favor of "safety", and tangentially mentioned that they didn't want to make the model too human-like. [1] Claude 3 was the last version of Claude, and one of the very few models in general, that had a character. That doesn't mean it wasn't writing slop though, falling into annoying stereotypes is still unsolved in LLMs.
[1] https://www.anthropic.com/research/claude-character (see the last 2 paragraphs)
Disagree, i've found kimi useful in solving creative coding problems gemini, claude, chatgpt etc failed at. Or, it is far better at verifying, augmenting and adding to human reviews of resumes for positions. It catches missed detials humans and other llm's routinley miss. There is something special to K2.
I tried this today. It's good - but it was significantly less focused and reliable than Opus 4.5 at implementing some mostly-fleshed-out specs I had lying around for some needed modifications to an enterprise TS node/express service. I was a bit disappointed tbh, the speed via fireworks.ai is great, they're doing great work on the hosting side. But I found the model had to double-back to fix type issues, broken tests, etc, far more than Opus 4.5 which churned through the tasks with almost zero errors. In fact, I gave the resulting code to Opus, simply said it looked "sloppy" and Opus cleaned it up very quickly.
It is amazing, but "open source model" means "model I can understand and modify" (= all the training data and processes).
Open weights is an equivalent of binary driver blobs everyone hates. "Here is an opaque thing, you have to put it on your computer and trust it, and you can't modify it."
That's unfair. Binary driver blobs are blackmail: "you bought the hardware, but parts of the laptop won't work unless you agree to run this mysterious bundle insecurely". Open weight is more like "here's a frozen brain you can thaw in a safe harness to do your bidding".
I tried Kimi 2.5 Swarm Agent version and it was way better than any AI model I've tried so far.
Kimi K2T was good. This model is outstanding, based on the time I've had to test it (basically since it came out). It's so good at following my instructions, staying on task, and not getting context poisoned. I don't use Claude or GPT, so I can't say how good it is compared to them, but it's definitely head and shoulders above the open weight competitors
Is there a reasonable place to run the unquantized version of this for less than Claude or OpenAI?
It seems to be priced the same and if it’s being hosted somewhere vs run locally it’s still a worse model, the only advantage would be it is not Anthropic or OpenAI.
It seems to work with OpenCode, but I can't tell exactly what's going on -- I was super impressed when OpenCode presented me with a UI to switch the view between different sub-agents. I don't know if OpenCode is aware of the capability, or the model is really good at telling the harness how to spawn sub-agents or execute parallel tool calls.
Yes. https://x.com/swyx/status/2016381014483075561?s=20 it's not crazy, they cap it to 3 credits, and also YSK agent swarm is a closed source product
Would i use it a gain compared to Deep Research products elsewhere? Maybe, probably not but only bc it's hard to switch apps
OpenAI is a household name with nearly a billion weekly active users. Not sure there's any reality where they wouldn't be valued much more than Kimi regardless of how close the models may be.
Well to be the devil's advocate: One is a household name that holds most of the world's silicon wafers for ransom, and the other sounds like a crypto scam. Also estimating valuation of Chinese companies is sort of nonsense when they're all effectively state owned.
I'm not sure if that is accurate, most of the funding they've got is from Tencent and Alibaba, and we know what happened to Jack Ma the second he went against the party line. These two are defacto state owned enterprises. Moonshot is unlikely to be for sale in any meaningful way so its valuation is moot.
[0] https://en.wikipedia.org/wiki/Moonshot_AI#Funding_and_invest...
I've been using kimi 2.5 to write Rust code and plan out detailed features. so far its brillient.
I’ve added the api key support to kimi on my agentic coding: https://github.com/tallesborges/zdx
A lot better in my experience. M2.1 to me feels between haiku and sonnet. K2.5 feels close to opus. That's based on my testing of removing some code and getting it to reimplement based on tests. Also the design/spec writing feels great. You can still test k2.5 for free in OpenCode today.
Calude give 100% passmark for code generated by kimi and sometimes it say, its better than what claude proposed. Absolutely best os model.
When will hardware get cheap enough so people can run this locally? That’s the world I’m waiting for.
I wonder how K2.5 + OpenCode compares to Opus with CC. If it is close I would let go of my subscription, as probably a lot of people.
It is not opus. It is good, works really fast and suprisingly through about its decisions. However I've seen it hallucinate things.
Just today I asked for a code review and it flagged a method that can be `static`. The problem is it was already static. That kind of stuff never happens with Opus 4.5 as far as I can tell.
Also, in an opencode Plan mode (read only). It generated a plan and instead of presenting it and stopping, decided to implement it. Could not use the edit and write tools because the harness was in read only mode. But it had bash and started using bash to edit stuff. Wouldn't just fucking stop even though the error messages it received from opencode stated why. Its plan and the resulting code was ok so I let it go crazy though...
I've been using K2.5 with OpenCode to do code assessments/fixes and Opus 4.5 with CC to check the work, and so far so good. Very impressed with it so far, but I don't feel comfortable canceling my Claude subscription just yet. Haven't tried it on large feature implementations.
yes, just use the base url https://api.moonshot.ai/anthropic
(https://platform.moonshot.ai/docs/guide/agent-support#config...)
I've been drafting plans/specs in parallel with Opus and Kimi. Then asking them to review the others plan.
I still find Opus is "sharper" technically, tackles problems more completely & gets the nuance.
But man Kimi k2.5 can write. Even if I don't have a big problem description, just a bunch of specs, Kimi is there, writing good intro material, having good text that more than elaborates, that actually explains. Opus, GLM-4.7 have both complemented Kimi on it's writing.
Still mainly using my z.ai glm-4.7 subscription for the work, so I don't know how capable it really is. But I do tend to go for some Opus in sticky spots, and especially given the 9x price difference, I should try some Kimi. I wish I was set up for better parallel evaluation; feels like such a pain to get started.
How do people evaluate creative writing and emotional intelligence in LLMs? Most benchmarks seem to focus on reasoning or correctness, which feels orthogonal. I’ve been playing with Kimmy K 2.5 and it feels much stronger on voice and emotional grounding, but I don’t know how to measure that beyond human judgment.
I am trying! https://mafia-arena.com
I just don't have enough funding to do a ton of tests
Sure. Someone on /r/LocalLLaMA was seeing 12.5 tokens/s on dual Strix Halo 128GB machines (run you $6-8K total?) with 1.8bits per parameter. It performs far below the unquantized model, so it would not be my personal pick for a one-local-LLM-forever, but it is compelling because it has image and video understanding. You lose those features if you choose, say, gpt-oss-120B.
Also, that's with no context, so it would be slower as it filled (I don't think K2.5 uses the Kimi-Linear KDA attention mechanism, so it's sub-quadratic but not their lowest).
30 people trying out all models on the list for their use case for a week and then checking what they're still using a month after.
DeepSeek is likely to release a new model soon, and judging from the past it's likely to be more cost effective and just as or more powerful than Kimi 2.5.
DeepSeek 3.2 was already quite compelling. I expect its successor will be competitive.
I'm not very familiar with this model series but tried it when prototyping a potential 'Vibesbench' (my eval) conversation I tried it. Here's Kimi on a Robocop scene
kimi k2 thinking turbo
"He’s not being poetic; he’s weaponizing nostalgia. Quoting Casablanca—the ultimate “beautiful sacrifice” romance—to taunt Morton about corporate backstabbing is pure executive sleaze. It’s the same move as his mahogany desk and flag pin: draping sentimental Americana over naked power. He’s essentially saying, “Isn’t it romantic, Bob? You fought the good fight, now you die.” "
grok 4.1
"So when Dick Jones smirks and says, “Time goes by, it’s the same old story… the fight for love and glory, huh, Bob?” that’s pure Ed Neumeier flexing his love of old Hollywood while simultaneously mocking how these psychopathic ’80s execs think they’re in some grand cinematic drama instead of just murdering a guy for market share."
chatgpt-4o
"The reason the Casablanca reference matters is RoboCop itself is partially a noir—and also a tragedy built inside a genre engine. [… ] So yeah, calling back to Casablanca is not just some meta-joke—it’s a knowing wink that RoboCop is a noir trapped in cybernetic skin. // Even Clarence Boddicker, for all his cartoonish sadism, plays like a twisted stand-in for a noir heavy—talking smooth, dropping nicknames, throwing out casual violence like it’s a punchline."
opus 4.5
"Jones is performing avuncular menace. He's the kind of executive who'd quote old movies while having you murdered, because it's all performance to him, all style. The substance is just power."
I've been using this model (as a coding agent) for the past few days, and it's the first time I've felt that an open source model really competes with the big labs. So far it's been able to handle most things I've thrown at it. I'm almost hesitant to say that this is as good as Opus.