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.
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.
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.
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.
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.
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 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.
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 , 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.
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).
Not OP but OpenCode and DeepInfra seems like an easy way.
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.
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!
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.
> 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.
The pitiful state of GPUs. $10K for a sloth with no memory.
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.
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.
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)
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.
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.
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.
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.
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?
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 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.
[deleted]
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
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.
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
The old Kimi K2 is better than GLM4.7
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.
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.
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.
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.
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 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.
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 , 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/
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).
Not OP but OpenCode and DeepInfra seems like an easy way.
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.
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
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?
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.
The pitiful state of GPUs. $10K for a sloth with no memory.
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.
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.
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)
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.
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.
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.
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.
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?
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 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...)
It's a decent model but works best with kimi CLI, not CC or others.
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.
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