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.
Also my experience. I've been going back and forth between Opus and Kimi for the last few days, and, at least for my CRUD webapps, I would say they are both on the same level.
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.
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).
There's no comparison. GLM 4.7 is fine and reasonably competent at writing code, but K2.5 is right up there with something like Sonnet 4.5. it's the first time I can use an open-source model and not immediately tell the difference between it and top-end models from Anthropic and OpenAI.
Kimi k2.5 is a beast, speaks very human like (k2 was also good at this) and completes whatever I throw at it. However, the glm quarterly coding plan is too good of a deal. The Christmas deal ends today, so I’d still suggest to stick to it. There will always come a better model.
From what people say, it's better than GLM 4.7 (and I guess DeepSeek 3.2)
But it's also like... 10x the price per output token on any of the providers I've looked at.
I don't feel it's 10x the value. It's still much cheaper than paying by the token for Sonnet or Opus, but if you have a subscribed plan from the Big 3 (OpenAI, Anthropic, Google) it's much better value for $$.
Comes down to ethical or openness reasons to use it I guess.
Exactly. For the price it has to beat Claude and GPT, unless you have budget for both. I just let GLM solve whatever it can and reserve my Claude budget for the rest.
It's waaay better than GLM 4.7 (which was the open model I was using earlier)! Kimi was able to quickly and smoothly finish some very complex tasks that GLM completely choked at.
Very much so. I'm using it for small personal stuff on my home PC. Nothing grand. Not having to worry about token usage has been great (previously was paying per API use).
I haven't stress tested it with anything large. Both at work and home, I don't give much free rein to the AI (e.g. I examine and approve all code changes).
Lite plan doesn't have vision, so you cannot copy/paste an image there. But I can always switch models when I need to.
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.
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.
You can run it on consumer grade hardware right now, but it will be rather slow. NVMe SSDs these days have a read speed of 7 GB/s (EDIT: or even faster than that! Thank you @hedgehog for the update), so it will give you one token roughly every three seconds while crunching through the 32 billion active parameters, which are natively quantized to 4 bit each. If you want to run it faster, you have to spend more money.
High end consumer SSDs can do closer to 15 GB/s, though only with PCI-e gen 5. On a motherboard with two m.2 slots that's potentially around 30GB/s from disk.
Edit: How fast everything is depends on how much data needs to get loaded from disk which is not always everything on MoE models.
Yes, RAID 0 or 1 could both work in this case to combine the disks. You would want to check the bus topology for the specific motherboard to make sure the slots aren't on the other side of a hub or something like that.
You need 600gb of VRAM + MEMORY (+ DISK) to fit the model (full) or 240 for the 1b quantized model. Of course this will be slow.
Through moonshot api it is pretty fast (much much much faster than Gemini 3 pro and Claude sonnet, probably faster than Gemini flash), though. To get similar experience they say at least 4xH200.
If you don't mind running it super slow, you still need around 600gb of VRAM + fast RAM.
It's already possible to run 4xH200 in a domestic environment (it would be instantaneous for most tasks, unbelievable speed). It's just very very expensive and probably challenging for most users, manageable/easy for the average hacker news crowd.
Expensive AND hard to source high end GPUs, if you manage to source for the old prices around 200 thousand dollars to get maximum speed I guess, you could probably run decently on a bunch of high end machines, for let's say, 40k (slow).
Open source models can be hosted by provider, in particular plenty of educational institutions host open source models. You get to choose whatever provider you trust. For instance I used DeepSeek R1 a fair bit last year but never on deepseek.com or through its API.
Open source models costs are determined only by electricity usage, as anyone can rent a GPU qnd host them
Closed source models cost x10 more just because they can
A simple example is Claude Opus, which costs ~1/10 if not less in Claude Code that doesn't have that price multiplier
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.
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!
I was using it for multi-hour tasks scripted via an self-written orchestrator on a small VM and ended up switching away from it because it would run slower and slower over time.
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.
> Doesn't list Kimi 2.5 and seems to be chat-only, not API, correct?
Yes, it is chat only, but that list is out of date - Kimi 2.5 (with or without reasoning) is available, as are ChatGPT 5.2, Gemini 3 Pro (Preview), etc
> 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.
I'm running the Q4_K_M quant on a xeon with 7x A4000s and I'm getting about 8 tok/s with small context (16k). I need to do more tuning, I think I can get more out of it, but it's never gonna be fast on this suboptimal machine.
you can add 1 more GPU so you can take advantage of tensor parallel. I get the same speed with 5 3090's with most of the model on 2400mhz ddr4 ram, 8.5tk almost constant. I don't really do agents but chat, and it holds up to 64k.
That is a very good point and I would love to do it, but I built this machine in a desktop case and the motherboard has seven slots. I did a custom water cooling manifold just to make it work with all the cards.
I'm trying to figure out how to add another card on a riser hanging off a slimsas port, or maybe I could turn the bottom slot into two vertical slots.. the case (fractal meshify 2 xl) has room for a vertical mounted card that wouldn't interfere with the others, but I'd need to make a custom riser with two slots on it to make it work. I dunno, it's possible!
I also have an RTX Pro 6000 Blackwell and an RTX 5000 Ada.. I'd be better off pulling all the A7000s and throwing both of those cards in this machine, but then I wouldn't have anything for my desktop. Decisions, decisions!
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.
That tends to work quite poorly because Claude Code does not use standard completions APIs. I tried it with Kimi, using litellm[proxy], and it failed in too many places.
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.
It's hard to judge from this particular question, but the K2.5 output looks at least marginally better AIUI, the only real problem with it is the snarky initial "That's very interesting" quip. Even then a British user would probably be fine with it.
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.
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.
I have been very impressed with this model and also with the Kimi CLI. I have been using it with the 'Moderato' plan (7 days free, then 19$). A true competitor to Claude Code with Opus.
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".
Not equivalent to the binary driver: you can modify it yourself with post training on your own data. So it sits somewhere between NVIDIA userspace drivers and Emacs, or Clade Code and codex-cli. We don’t have good analogies from older generation software.
Do any of these models do well with information retrieval and reasoning from text?
I'm reading newspaper articles through a MoE of gemini3flash and gpt5mini, and what made it hard to use open models (at the time) was a lack of support for pydantic.
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
Sorry if this is an easy-answerable question - but by open we can download this and use totally offline if now or in the future if we have hardware capable? Seems like a great thing to archive if the world falls apart (said half-jokingly)
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).
Is the software/drivers for networking LLMs on Strix Halo there yet? I was under the impression a few weeks ago that it's veeeery early stages and terribly slow.
Yes but the hardware to run it decently gonna cost you north of $100k, so hopefully you and your bunkermates allocated the right amount to this instead of guns or ammo.
I really like the agent swarm thing, is it possible to use that functionality with OpenCode or is that a Kimi CLI specific thing? Does the agent need to be aware of the capability?
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.
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.
The Agent Swarm section is fascinating. I'm working on authorization for multi-agent systems so this is relevant to my interests. Lots of interesting parallels to capability-based security models.
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.
There isn't a single % that is state owned in Moonshot AI.
And don't start me with the "yeah but if the PRC" because it's gross when US can de facto ban and impose conditions even on European companies, let alone the control it has on US ones.
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.
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.
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.
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.
This Kimi K2 is so far the best. Gemini is also great, but google is stock in the academic bias of Stanford and MIT and can't think outside the box. China definitely ahead on Ai. Wish somehow someone here in the US, would think different.
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.
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.
Also my experience. I've been going back and forth between Opus and Kimi for the last few days, and, at least for my CRUD webapps, I would say they are both on the same level.
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).
https://z.ai/subscribe
There's no comparison. GLM 4.7 is fine and reasonably competent at writing code, but K2.5 is right up there with something like Sonnet 4.5. it's the first time I can use an open-source model and not immediately tell the difference between it and top-end models from Anthropic and OpenAI.
Kimi k2.5 is a beast, speaks very human like (k2 was also good at this) and completes whatever I throw at it. However, the glm quarterly coding plan is too good of a deal. The Christmas deal ends today, so I’d still suggest to stick to it. There will always come a better model.
From what people say, it's better than GLM 4.7 (and I guess DeepSeek 3.2)
But it's also like... 10x the price per output token on any of the providers I've looked at.
I don't feel it's 10x the value. It's still much cheaper than paying by the token for Sonnet or Opus, but if you have a subscribed plan from the Big 3 (OpenAI, Anthropic, Google) it's much better value for $$.
Comes down to ethical or openness reasons to use it I guess.
Exactly. For the price it has to beat Claude and GPT, unless you have budget for both. I just let GLM solve whatever it can and reserve my Claude budget for the rest.
It's waaay better than GLM 4.7 (which was the open model I was using earlier)! Kimi was able to quickly and smoothly finish some very complex tasks that GLM completely choked at.
The old Kimi K2 is better than GLM4.7
Is the Lite plan enough for your projects?
Very much so. I'm using it for small personal stuff on my home PC. Nothing grand. Not having to worry about token usage has been great (previously was paying per API use).
I haven't stress tested it with anything large. Both at work and home, I don't give much free rein to the AI (e.g. I examine and approve all code changes).
Lite plan doesn't have vision, so you cannot copy/paste an image there. But I can always switch models when I need to.
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.
How long until this can be run on consumer grade hardware or a domestic electricity supply I wonder.
Anyone have a projection?
You can run it on consumer grade hardware right now, but it will be rather slow. NVMe SSDs these days have a read speed of 7 GB/s (EDIT: or even faster than that! Thank you @hedgehog for the update), so it will give you one token roughly every three seconds while crunching through the 32 billion active parameters, which are natively quantized to 4 bit each. If you want to run it faster, you have to spend more money.
Some people in the localllama subreddit have built systems which run large models at more decent speeds: https://www.reddit.com/r/LocalLLaMA/
High end consumer SSDs can do closer to 15 GB/s, though only with PCI-e gen 5. On a motherboard with two m.2 slots that's potentially around 30GB/s from disk. Edit: How fast everything is depends on how much data needs to get loaded from disk which is not always everything on MoE models.
Would RAID zero help here?
Yes, RAID 0 or 1 could both work in this case to combine the disks. You would want to check the bus topology for the specific motherboard to make sure the slots aren't on the other side of a hub or something like that.
You need 600gb of VRAM + MEMORY (+ DISK) to fit the model (full) or 240 for the 1b quantized model. Of course this will be slow.
Through moonshot api it is pretty fast (much much much faster than Gemini 3 pro and Claude sonnet, probably faster than Gemini flash), though. To get similar experience they say at least 4xH200.
If you don't mind running it super slow, you still need around 600gb of VRAM + fast RAM.
It's already possible to run 4xH200 in a domestic environment (it would be instantaneous for most tasks, unbelievable speed). It's just very very expensive and probably challenging for most users, manageable/easy for the average hacker news crowd.
Expensive AND hard to source high end GPUs, if you manage to source for the old prices around 200 thousand dollars to get maximum speed I guess, you could probably run decently on a bunch of high end machines, for let's say, 40k (slow).
You can run it on a mac studio with 512gb ram, that's the easiest way. I run it at home on a multi rig GPU with partial offload to ram.
I was wondering whether multiple GPUs make it go appreciably faster when limited by VRAM. Do you have some tokens/sec numbers for text generation?
What's the point of using an open source model if you're not self-hosting?
Open source models can be hosted by provider, in particular plenty of educational institutions host open source models. You get to choose whatever provider you trust. For instance I used DeepSeek R1 a fair bit last year but never on deepseek.com or through its API.
Open source models costs are determined only by electricity usage, as anyone can rent a GPU qnd host them Closed source models cost x10 more just because they can A simple example is Claude Opus, which costs ~1/10 if not less in Claude Code that doesn't have that price multiplier
But Kimi seems so big that renting the necessary number of GPUs is a non trivial exercise.
Exactly! Electricity, hosting, and amortized cost of the GPUs would be the baseline costs.
* It's cheaper than proprietary models
* Maybe you don't want to have your conversations used for training. The providers listed on OpenRouter mention whether they do that or not.
Not OP but OpenCode and DeepInfra seems like an easy way.
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.
Note that Kimi K2x is natively 4 bit int, which reduces the memory requirements somewhat.
Just pick up any >240GB VRAM GPU off your local BestBuy to run a quantized version.
> The full Kimi K2.5 model is 630GB and typically requires at least 4× H200 GPUs.
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.
That would be quite the space heater, too!
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
But it may be a quantized model for the free version.
Can you share how you're running it?
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!
https://github.com/MoonshotAI/kimi-cli
I like Kimi-cli but it does leak memory.
I was using it for multi-hour tasks scripted via an self-written orchestrator on a small VM and ended up switching away from it because it would run slower and slower over time.
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.
[1] https://help.kagi.com/kagi/ai/assistant.html
Doesn't list Kimi 2.5 and seems to be chat-only, not API, correct?
> Doesn't list Kimi 2.5 and seems to be chat-only, not API, correct?
Yes, it is chat only, but that list is out of date - Kimi 2.5 (with or without reasoning) is available, as are ChatGPT 5.2, Gemini 3 Pro (Preview), etc
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.
I'm running the Q4_K_M quant on a xeon with 7x A4000s and I'm getting about 8 tok/s with small context (16k). I need to do more tuning, I think I can get more out of it, but it's never gonna be fast on this suboptimal machine.
you can add 1 more GPU so you can take advantage of tensor parallel. I get the same speed with 5 3090's with most of the model on 2400mhz ddr4 ram, 8.5tk almost constant. I don't really do agents but chat, and it holds up to 64k.
That is a very good point and I would love to do it, but I built this machine in a desktop case and the motherboard has seven slots. I did a custom water cooling manifold just to make it work with all the cards.
I'm trying to figure out how to add another card on a riser hanging off a slimsas port, or maybe I could turn the bottom slot into two vertical slots.. the case (fractal meshify 2 xl) has room for a vertical mounted card that wouldn't interfere with the others, but I'd need to make a custom riser with two slots on it to make it work. I dunno, it's possible!
I also have an RTX Pro 6000 Blackwell and an RTX 5000 Ada.. I'd be better off pulling all the A7000s and throwing both of those cards in this machine, but then I wouldn't have anything for my desktop. Decisions, decisions!
The pitiful state of GPUs. $10K for a sloth with no memory.
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.
Yeah I too am curious. Because Claude code is so good and the ecosystem so just it works that I’m Willing to pay them.
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!
You can plug another model in place of Anthropic ones in Claude Code.
That tends to work quite poorly because Claude Code does not use standard completions APIs. I tried it with Kimi, using litellm[proxy], and it failed in too many places.
You can try Kimi's Anthropic-compatible API.
Just connect Claude Code to Kimi's API endpoint and everything works well
https://www.kimi.com/code/docs/en/more/third-party-agents.ht...
It worked very well for me using qwen3 coder behind a litellm. Most other models just fail in weird ways though.
opencode is a good alternative that doesnt flake out in this way.
If you don't use Antrophic models there's no reason to use Claude Code at all. Opencode gives so much more choice.
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)
It's hard to judge from this particular question, but the K2.5 output looks at least marginally better AIUI, the only real problem with it is the snarky initial "That's very interesting" quip. Even then a British user would probably be fine with it.
I agree. K2 was blunt, straightforward, pretty... rational? K2.5 has a much stronger slop vibe.
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)
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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.
I have been very impressed with this model and also with the Kimi CLI. I have been using it with the 'Moderato' plan (7 days free, then 19$). A true competitor to Claude Code with Opus.
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".
Not equivalent to the binary driver: you can modify it yourself with post training on your own data. So it sits somewhere between NVIDIA userspace drivers and Emacs, or Clade Code and codex-cli. We don’t have good analogies from older generation software.
Do any of these models do well with information retrieval and reasoning from text?
I'm reading newspaper articles through a MoE of gemini3flash and gpt5mini, and what made it hard to use open models (at the time) was a lack of support for pydantic.
That roughly correlates with tool calling capabilities. Kimi K2.5 is a lot better than previous open source models in that regard.
You should try out K2.5 for your use case, it might actually succeed where previous generation open source models failed.
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
Sorry if this is an easy-answerable question - but by open we can download this and use totally offline if now or in the future if we have hardware capable? Seems like a great thing to archive if the world falls apart (said half-jokingly)
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).
You could buy five Strix Halo systems at $2000 each, network them and run it.
Rough estimage: 12.5:2.2 so you should get around 5.5 tokens/s.
Is the software/drivers for networking LLMs on Strix Halo there yet? I was under the impression a few weeks ago that it's veeeery early stages and terribly slow.
Llama.cpp with its rpc-server
Yes but the hardware to run it decently gonna cost you north of $100k, so hopefully you and your bunkermates allocated the right amount to this instead of guns or ammo.
Yes.
Yes, but you'll need some pretty massive hardware.
I really like the agent swarm thing, is it possible to use that functionality with OpenCode or is that a Kimi CLI specific thing? Does the agent need to be aware of the capability?
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.
Has anyone tried it and decided it's worth the cost; I've heard it's even more profligate with tokens?
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
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.
The Agent Swarm section is fascinating. I'm working on authorization for multi-agent systems so this is relevant to my interests. Lots of interesting parallels to capability-based security models.
It's interesting to note that a model that can OpenAI is valued almost 400 times more than moonshotai, despite their models being surprisingly close.
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.
There isn't a single % that is state owned in Moonshot AI.
And don't start me with the "yeah but if the PRC" because it's gross when US can de facto ban and impose conditions even on European companies, let alone the control it has on US ones.
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...
Funny because that's how us Americans feel about your European cookie banner litter and unilateral demands on privacy
Unless they can beat their capabilities by a clear magical step up and has infrastructure to capture the users
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...
Some models have a mind of their own. I keep them on a leash with `permission` blocks in OC -- especially for rm/mv/git.
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.
I also wonder if CC can be used with k2.5 with the appropriate API adapter
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.
I've been quite satisfied lately with MiniMax M-2.1 in opencode.
How does Kimi 2.5 compare to it in real world scenarios?
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.
Well, Minimax was the equivalent of Sonnet in my testing. If Kimi approach Opus, that would be great.
Kimi K2.5 approaches Sonnet as well from what I can tell, it's just slower to get to the result.
This Kimi K2 is so far the best. Gemini is also great, but google is stock in the academic bias of Stanford and MIT and can't think outside the box. China definitely ahead on Ai. Wish somehow someone here in the US, would think different.
> but google is stock in the academic bias of Stanford and MIT and can't think outside the box
Can you clarify what you mean? I am not sure I follow.
s/stock/stuck/
I’ve added the api key support to kimi on my agentic coding: https://github.com/tallesborges/zdx
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I've been using kimi 2.5 to write Rust code and plan out detailed features. so far its brillient.
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.
2042. But by then you won't want to run this model anymore.
It's a decent model but works best with kimi CLI, not CC or others.
I haven't use Kimi CLI, but it works very well with OpenCode.
Why do you think that is?
I heard it's because the labs fine tune their models for their own harness. Same reason why claude does better in claude code than cursor.
read the tech report
Do I need to have two M3U 512GB MacStudios to run this?
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.
https://eqbench.com/index.html
I am trying! https://mafia-arena.com
I just don't have enough funding to do a ton of tests
I wonder if there will be smaller version of the model that can run on Strix Halo in 128GB.
The benchmarks on all these models are meaningless
Why and what would a good benchmark look like?
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."
Love to see Open source models doing better than SOTA