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Kimi K2.6 just beat Claude, GPT-5.5, and Gemini in a coding challenge

380 points by bazlightyear 6mo ago

magicalhippo 6mo ago
In a single challenge, measured by how performant the solution was. Kimi K2.6 is definitely a frontier-sized model, so on the one hand it's not that surprising it's up there with the closed frontier models. Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU.
- echelon 6mo ago
  This is the future though. Open weights models that run on H200s provide far more opportunity to build products and real infrastructure around. You can always distill this for your little RTX at home. But models shaped for consumer hardware will never win wide adoption or remain competitive with frontier labs. This is something that _can_ compete. And it will both necessitate and inspire a new generation of open cloud infra to run inference. "Push button, deploy" or "Push button, fine tune" shaped products at the start, then far more advanced products that only open weights not locked behind an API can accomplish. Now we just need open weights Nano Banana Pro / GPT Image 2, and Seedance 2.0 equivalents. The battle and focus should be on open weights for the data center.
  - bitmasher9 6mo ago
    I don’t fully understand what open weights unlocks that cannot be accomplished via API from a product standpoint. Open weights is great if you want to do additional training, or if you need on-prem for security.
    - stldev 6mo ago
      Or try to beat Anthropic's uptime.
    - mkl 6mo ago
      Multiple providers of the same model. That means competition for price, reliability, latency, etc. It also means you can use the same model as long as you want, instead of having it silently change behaviour.
      - Bombthecat 6mo ago
        Those open weight providers where found nerfing models too.
    - echelon 6mo ago
      > Open weights is great if you want to do additional training, or if you need on-prem for security. The power of giving universities, companies, and hackers "full" models should not be understated. Here are a just a few ideas for image, video, and creative media models: - Suddenly you're not "blocked" for entire innocuous prompts. This is a huge issue. - You can fine tune the model to learn/do new things. A lighting adjustment model, a pose adjustment model. You can hook up the model to mocap, train it to generate plates, etc. - You can fine tune it on your brand aesthetic and not have it washed out.
  - zozbot234 6mo ago
    These large MoE models can work quite well on consumer or prosumer platforms, they'll just be slow, and you have to offset that by running them unattended around the clock. (Something that you can't really do with large SOTA models without spending way too much on tokens.) This actually works quite well for DeepSeek V4 series which has comparatively tiny KV-cache sizes so even a consumer platform can run big batches in parallel.
- joshoink 6mo ago
  [flagged]
- keyle 6mo ago
  It absolutely does matter. The enshittification will go unnoticed at first but I'm already finding my favourite frontier models severely nerfed, doing incredibly dumb stuff they weren't in the past. We need open weight models to have a stable "platform" when we rely on them, which we do more and more.
  - magicalhippo 6mo ago
    Most people won't roll out their own K2 deployment across rented GPUs, so in that sense it doesn't matter that much, they'll be using a paid service which is just as much of a black box as Claude or ChatGPT. For example, on OpenRouter you can select a provider which state they use a given open model, but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on. That said, I do fully agree that it is valuable to have open near-frontier models, as a balance to the closed ones.
    - slopinthebag 6mo ago
      It's not really a black box. Useful models becoming fungible is crucial for disincentivizing bad behaviour with model providers. I can't really overstate how different it is from relying on closed models. If you don't like or trust any of the providers on OpenRouter you can rent the GPUs yourself and host it, although this is probably unnecessary.
    - roenxi 6mo ago
      > but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on. That would take something close to a global conspiracy of every technologist lying continuously to keep the tweaks secret. If necessary, I personally will rent some servers and run a vanilla Kimi K2.6 deployment for people to use at reasonable prices. I don't expect to ever make good on that threat because they are grim times indeed if I'm the first person doing something AI related, but the skill level required to load up a model behind an API is low. So it isn't hard to see how there will be unadulterated Kimi models available and from there it is really, really straightfoward to tell if someone is quantising a model; just run some benchmarks against 2 different providers who both claim to serve the same thing. If one is quantising and another isn't there's a big difference in quality.
- DeathArrow 6mo ago
  >Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU. Of course it matters because that makes coding plans much cheaper than those from Anthropic and OpenAI. For personal use I have coding plans with GLM 5.1, Kimi K2.6, MiniMax M2.7 and Xiaomi MiMo V2.5 Pro and I am getting a lot of bang for the buck.
  - magicalhippo 6mo ago
    Currently it's not a huge difference given the subsidies of closed model subscriptions. Once that stops then yea it will be really nice to have open models as price competitors.
    - smj-edison 6mo ago
      At least in my experience switching from Claude Pro ($20/month) to Kimi 2.6 through ollama (also $20/month), I was almost always hitting my usage limit with Sonnet 4.6, but with ollama I haven't hit my usage a single time.
      - ode 6mo ago
        How many t/ps do you get with Kimi on Ollama?
        smj-edison 6mo ago
        I actually have no clue how I'd check, do you know how?
    - DeathArrow 6mo ago
      >Currently it's not a huge difference given the subsidies of closed model subscriptions. With Claude Max I was hitting the limits very fast.
- lelanthran 6mo ago
  > Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU. The value of open source is not that you will run it locally, it's that anyone can run it at all. Even if you can't afford to purchase the hardware to run large open source models, someone would, price it at half the cost of the closed source models and still make a profit. The only reason you are not seeing that happen right now is because the current front-running token-providers have subsidised their inference costs. The minute they start their enshittification the market for alternatives becomes viable. Without open-source models, there will never be a viable alternative. Even if they wanted to charge only 80% of what a developer costs, the existence of open source models that are not far behind is a forcing function on them. There is no moat for them.
  - 0xkvyb 6mo ago
    Totally agree with you. There is only so much time before SF tech runs out of subsidy bucks, and Chinese models take the consumer spotlight
  - yorwba 6mo ago
    The reason nobody is pricing Kimi K2.6 at half the cost of the best closed source models is that there are too many providers of the same model, so the competition drives prices down and they have to charge far less than that. https://openrouter.ai/moonshotai/kimi-k2.6/providers https://openrouter.ai/moonshotai/kimi-k2.6/providers
- tom2026hn 6mo ago
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PedroBatista 6mo ago
Great to know, but what was the cost both in terms of $$ and tokens used? Not to invalidate these benchmark results because they are useful, but the real usefulness it what they are capable to do when real people interact with them at scale. Regardless, these are good news, because now that Microsoft is basically giving up their all-in strategy with Github's Copilot and Anthropic is playing the "I'm too good for you" game, it's about time for them to get pressed into not making this AI world into a divide between the haves and the have-nots.
- keyle 6mo ago
  Re pricing. Never as high as frontier commercial models.
  - CryptoBanker 6mo ago
    You’d be surprised with some long running complex tasks. I’ve seen Kimi spend 8 minutes (total) thinking on a task that Claude got done in 30 seconds. They both ultimately got it right, but Kimi spent ~$2.25 to Claude’s ~$0.20
beering 6mo ago
I’m a little confused as to the setup. It was asking each model to one-shot a script and then the scripts faced off? Were the models given a computer environment? Or a test server to iterate against?
- rpmisms 6mo ago
  Sounds incredibly simple to me. One-shot.
  - beering 6mo ago
    So nothing like real-world coding, where you’d be able to run and test the script before submitting?
    - procinct 6mo ago
      One shot just means the user doesn’t have to iterate on it via the agent. The agent does what ever it needs to deliver the best outcome, including its own running and iteration until it’s happy with it. This could be a short or long process potentially depending on the task.
Frannky 6mo ago
I have to try Kimi. I was looking for an alternative. If you have any experience, advice, please share. I saw Kimi is at the top of the Open Router ranking.
- zorked 6mo ago
  I use Kimi at home via a kimi.com subscription and Kimi CLI (sometimes running inside Zed, sometimes not). My favorite model by far. And it's just $20. I have to use a supposedly frontier model at work and I hate it.
  - Frannky 6mo ago
    Nice, thanks for sharing!
- DeathArrow 6mo ago
  Kimi K2.6 is great but I advice you to get a coding plan from Kimi.com as that way is much cheaper than paying for API calls using OpenRouter.
  - Frannky 6mo ago
    Thanks, I am trying it right now. I had an opencode plan 5$/month, so I will play with that. I use ZED and I added Pi ACP, so I can try the both pi and Kimi. I will also try it in opencode and via Kimi code.
    - prvnsmpth 6mo ago
      Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.
      - Frannky 6mo ago
        Very interesting, thanks for sharing. I am testing it with Pi in Zed and it seems pretty good.
elromulous 6mo ago
Is the site just slashdotted rn? Can anyone get to it?
- brettgo1 6mo ago
  Slashdot... Now that's a name I haven't heard in a long time. A long time.
deleted 6mo ago
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surrTurr 6mo ago
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jakemanger 6mo ago
What's the GPU VRAM requirements for this thing? Awesome to have a open model that can compete, but damn it would be so much better if you could run it locally. Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc
- DeathArrow 6mo ago
  >Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc Getting a coding plan from Kimi.com will make coding 20x cheaper than using Anthropic. BTW, I am using it with Claude Code.
- redrove 6mo ago
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slashdave 6mo ago
I was surprised by the ranking, until I read what the test was. Not horribly relevant for coding. The current ranking of all tests makes more sense (well, except for how well Gemini does) https://aicc.rayonnant.ai https://aicc.rayonnant.ai
- mpeg 6mo ago
  If you look at the ranking breakdown though, Kimi K2.6 has only participated in the last 5 challenges (claude dominated before then) and if you only count those it would be in first place
  - Sammi 6mo ago
    It also has a DNF. So it has a high ceiling but also unfortunately a low floor. So using Kimi means accepting high variability of the output. Personally what I've found that has made coding agents more and more useful over the last year is that they have gotten a higher and higher floor, not that they have gotten a higher and higher ceiling. They were already plenty smart a year ago, it was just that they failed so often and so spectacularly that it made them a liability. Now they have become much more reliable, which is the key thing that has transitioned them into being actually useful. For the most part I don't use them to work on really intellectually difficult tasks. I mostly use them to work on very boring and labor intensive tasks. Most commercial software development work is just boring drudgery like this. Certainly the bulk of what I need them for is. I need them to just not crap their pants all the time while they're at it. So I'm kinda wary seeing the poor reliability of Kimi.
    - mpeg 6mo ago
      If you look at the last 5 challenges (the ones Kimi was in) both Claude and Kimi have 1 DNF, chatgpt has 2 I'm not sure this is enough data to form an opinion, but going by what we have Kimi would be as reliable as Claude
- SeriousM 6mo ago
  The ranking of gold medals only makes sense if all models would gave participate all tests. DNP = Did not participate In this regard, kimi got more and better medals than Claude.
pbreit 6mo ago
All my co-workers say Claude blows away Gemini. Is it really that good? How can I do Kimi?
- prvnsmpth 6mo ago
  You can sign up for a plan on the kimi code platform and use it via the pi.dev coding agent, or opencode. In planning, I’d say it’s almost on par with Claude Opus.
Rekindle8090 6mo ago
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justech 6mo ago
I’ve been maining Kimi k2.6 through opencode go and openrouter for a week and I can say it’s the same experience as when I was maining Sonnet 3.5/4 late last year. Not as good or as fast as Claude Code on Opus now but definitely enough for casual/hobby use. The best part is multiple choices for providers, if opencode gimps their service, I’ll switch
jrecyclebin 6mo ago
I absolutely love Kimi's personality - some of the things it says are so out there! And it's been great for very focused, iterative work. Its weakness is that it seems to yak on-and-on when it needs to plan out something big or read through and make sense of how to use a niche piece of a complex library. To the point where it can fill up its 256k window - and rack up a build. (No cache.) I have had better experience with GLM 5.1 in those cases. Anyone out there relate?
- anderber 6mo ago
  Absolutely. I use caveman to help with that: https://github.com/JuliusBrussee/caveman https://github.com/JuliusBrussee/caveman
  - jrecyclebin 6mo ago
    Not a bad idea - however > Caveman only affects output tokens — thinking/reasoning tokens are untouched. The problem is the thinking. But could help to tune my system prompt for Kimi.
  - LeoPanthera 6mo ago
    You can just add "be brief" to the prompt to replace the entire plugin. Same results. https://www.maxtaylor.me/articles/i-benchmarked-caveman-against-two-words https://www.maxtaylor.me/articles/i-benchmarked-caveman-agai...
gertlabs 6mo ago
I'm glad we're seeing a shift towards objectively scored tests. We've been doing this at scale at https://gertlabs.com/rankings https://gertlabs.com/rankings, and although the author looks to be running unique one-off samples, it's not surprising to see how well Kimi K2.6 performed. Based on our testing, for coding especially, Kimi is within statistical uncertainty of MiMo V2.5 Pro for top open weights model, and performs much better with tools than DeepSeek V4 Pro. GPT 5.5 has a comfortable lead, but Kimi is on par with or better than Opus 4.6. The problem with Kimi 2.6 is that it's one of the slower models we've tested.
- veber-alex 6mo ago
  In my experience benchmarks are pretty meaningless. Not only is performance dependent on the language and tasks gives but also the prompts used and the expected results. In my own internal tests it was really hard to judge whether GPT 5.5 or Opus 4.7 is the better model. They have different styles and it's basically up to preference. There where even times where I gave the win to one model only to think about it more and change my mind. At the end of the day I think I slightly prefer Opus 4.7.
  - gertlabs 6mo ago
    I think benchmarks are improving and will always have value, but it's the equivalent to someone's college and GPA for an entry level job application. It's a strong signal for a job, but the soft skills are sometimes going to get Claude Opus 4.6 a job over smarter applicants. That's what we'd really like to measure objectively, and are actively working on.
  - magicalhippo 6mo ago
    In addition, the harness around these models do a lot of work and changes the outcome significantly. I just had an issue where Claude CLI with Opus 4.7 High could not figure out why my Blazor Server program was inert, buttons didn't do anything etc. After several rounds, I opened the web console and found that it failed to load blazor.js due to 404 on that file. I copied the error message to Claude CLI and after another several unproductive rounds I gave up. I then moved the Codex, with ChatGTP 5.5 High. I gave it the code base, problem description and error codes. Unlike Claude CLI it spun up the project and used wget/curl to probe for blazor.js, and found indeed it was not served. It then did a lot more probing and some web searches and after a while found my project file was missing a setting. It added that and then probed to verify it worked. So Codex fixed it in about 20 minutes without me laying hands on it (other than approve some program executions). However, I'm not convinced this shows GPT 5.5 being that much better than Opus 4.7. It could very well be the harness around it, the system prompts used in the harness and tools available. For reference this was me just trying to see how good the vibecoding experience is now, so was trying to do this as much hands-off as possible.
SomaticPirate 6mo ago
This seems to be testing the models on leetcode style prompts that also require the model to implement TCP calls to send the results. Interesting but probably not a apples to apples comparison. The fact only Grok qualified for the first one seems suspect
aykutseker 6mo ago
This seems less like Kimi is better at coding than Claude and more like Kimi found the right strategy for this particular game. Still interesting though. The fact that an open weight model is close enough for that to matter is probably the real story.
rvz 6mo ago
So we are now at the point where open weight models are rapidly catching up to the frontier models. They are at best 30 days behind, and at worst case 2 months behind. The last issue is being able to run the best one on conventional hardware without a rack of GPUs. The Macbooks, and Mac minis are behind on hardware but eventually in the next 2 years at worst will make it possible thanks to the advancements of the M-series machines. All of this is why companies like Anthropic feel like they have to use "safety" to stop you from running local models on your machine and get you hooked on their casino wasting tokens with a slot machine named Claude.
qakajjqj 6mo ago
Yes gimini is a programming application
0xbadcafebee 6mo ago
These posts are going to be a constant for the next year, because there's no objective way to compare models (past low-level numbers like token generation speed, average reasoning token amount, # of parameters, active experts, etc). They're all quite different in a lot of ways, they're used for many different things by different people, and they're not deterministic. So you're constantly gonna see benchmarks and tests and proclamations of "THIS model beat THAT model!", with people racing around trying to find the best one. But there is no best one. There's just the best one for you, based on whatever your criteria is. It's likely we'll end up in a "Windows vs MacOS vs Linux" style world, where people stick to their camps that do a particular thing a particular way.
- verve_rat 6mo ago
  My theory is we will end up in a similar spot to hiring people. You can look at a CV (benchmarks) but you won't know for sure until you've worked with them for six months. We as an industry cannot determine if one software engineer is objectively better than another, on practically any dimension, so why do we think we can come to an objective ranking of models?
  - zelphirkalt 6mo ago
    Not many things are as manifold broken as hiring these days. I hope we do not end up there.
    - deleted 6mo ago
      [deleted]
  - pishpash 6mo ago
    You do not interview 1000 rounds on problems you're actually solving. If you did, hiring would be fine. Minus the social fit aspect, which isn't as relevant for a model.
  - roymain 6mo ago
    The CV-to-six-months analogy is actually exactly right and it's also why benchmarks for hiring people stopped being useful. The signal that holds up is what you see when something breaks, which is hard to compress into a number.
slopinthebag 6mo ago
Amazing. To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Truly a great time to be alive. I would like to see more effort making the flash variants work for coding. They are super economical to use to brute force boilerplate and drudgery, and I wonder just how good they can be with the right harness, if it provides the right UX for the steering they require. As much as vibe coding has captured the zeitgeist, I think long term using them as tools to generate code at the hands of skilled developers makes more sense. Companies can only go so long spending obscene amounts of money for subpar unmaintainable code.
- Barbing 6mo ago
  >To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Yes, at least probably with each other >Truly a great time to be alive. Do wish outcome had been better for those whose work ended up in the training sets, wish competition could’ve found ways to agree on distillation practices, wish globally we’d planned as fast as we’re developing… Tremendous excitement too
walrus01 6mo ago
People thinking to self-host Kimi K2.6 had better be prepared for how big it is. Q8 K XL quantization for instance is around 600GB on disk. I would bet about 700GB of VRAM needed. Quantizations lower than Q8 are probably worthless for quality. Or 2.05TB on disk for the full precision GGUF. https://huggingface.co/unsloth/Kimi-K2.6-GGUF https://huggingface.co/unsloth/Kimi-K2.6-GGUF If you can afford the hardware to run Kimi K2.6 at any decent speed for more than 1 simultaneous user, you probably have a whole team of people on staff who are already very familiar with how to benchmark it vs Claude, GPT-5.5, etc.
- zozbot234 6mo ago
  Kimi is a natively quantized model, the lossless full precision release is 595GB. Your own link mentions that.
  - CamperBob2 6mo ago
    So, realistically, $100K for an 8x RTX 6000 Pro system that can run it at a usable rate.
    - zozbot234 6mo ago
      I think people will always disagree on what qualifies as a "usable rate". But keep in mind that practically no one sensible is running the latest Opus or GPT around the clock, especially not at sustainable, unsubsidized prices. With open-weights models it's easy to do that.
      - walrus01 6mo ago
        Also for people doing something medical, privacy or sensitive data related, there's an almost incalculable value (depending on industry niche) in having absolutely no external network traffic to any servers/systems you don't fully control.
  - walrus01 6mo ago
    the 'unsloth' link above is a 3rd party person that has quantized it to Q8, the original release is considerably larger in size than 600GB: https://huggingface.co/moonshotai/Kimi-K2.6 https://huggingface.co/moonshotai/Kimi-K2.6
sieve 6mo ago
Kimi is really good. I have been using Sonnet and others (DeepSeek, ChatGPT, MiniMax, Qwen) for my compiler/vm project and the Claude Pro plan is mostly unusable for any serious coding effort. So I use it in chat mode in the browser where it cannot needlessly read your entire project, and use Kimi on the OpenCode Go plan with pi. Kimi consistently exceeded Sonnet on the C+Python project. Never had to worry about it doing anything other than what I asked it to do. GLM crapped the bed once or twice. Kimi never did.
- joe_mamba 6mo ago
  >the Claude Pro plan is mostly unusable for any serious coding effort Why? Seems to go a giant the opinion of the masses who mostly use Claude Pro for serious coding.
  - atraac 6mo ago
    Anecdotal evidence but last week or two Claude changed something related to their quotas. I'm a Pro user(now Team Standard) and while I did quite a lot before with that subscription, past week the 5h quota quite literally lasts maybe 5 semi sized prompts. I don't "vibe" anything, I give it clearly defined tasks or things to debug/fix, nothing hardcore. I ran out of the quota every single day past week, often twice a day, this never happened before. It's rather unusable for actual professional usage now. I'm tempted to test Codex over next week to compare hence why we're waiting with going to Claude Max sub.
  - LUmBULtERA 6mo ago
    I think ths comment is referring to the usage limits of the Pro plan. You run out of usage very fast if on anything less than a Max ($100+/month) plan.
  - sieve 6mo ago
    Claude is opaque as regards token usage. So you might end up using your 5hr limit in 7-10 minutes using regular Sonnet. Meanwhile, OpenCode etc give you exact breakdown in terms of how many cached tokens used per session etc which you can use to estimate burn rate. All these coding tools are extremely wasteful as far as resources are concerned. Almost designed to make you move to the next tier. You have to consciously restrict their scope all the time to make your plans last. Even with Kimi/MiniMax a 3-4 hour session often ends up with 50-70M cached reads. Not a small amount at all.
plexescor 6mo ago
I always though claude is the goat, but i guess its time to change the notion and try Kimi K2.6
koala-news 6mo ago
In my opinion, this kind of comparison is not very meaningful.
ninjahawk1 6mo ago
At the current rate, open sourced models are expected to surpass cloud models within a couple years based on a study I read a couple days ago. Looking back at chatGPT and claude a couple years ago, very small Qwen models are basically equal in coding to what those cloud based models could do then. Also factoring in scaling laws, a 9b going to 18b is roughly a 40% increase, whereas 18b to 35b is 20%, I expect there will be a change of at least price in cloud based models. Adobe used to be $600 per month, then it became $20 when distribution scaled.
- baxtr 6mo ago
  While this might be true I’m worried about the hardware side of things. What if you have a good enough model but the cloud model providers are better in procuring hardware for interference?
  - gleenn 6mo ago
    Local inference is definitely going to make more and more sense. Modern CPUs have all this amazing hardware well-optimized for inference purposes. I use a lot of web tools and see AI baked in and it feels weird. I want the smartness localized for speed and data security. I think and hope the industry points towards smart ai agents operating as locally as possible.
  - zozbot234 6mo ago
    The cloud providers are probably better at procuring hardware for inference, but on prem users are better at repurposing hardware that they'd need anyway for their existing uses. In a world where AI compute is likely inherently scarce, it makes sense to rely on both.
  - Gigachad 6mo ago
    You’ll be able to run the open models on any cloud at the cost of the hardware rental. While the closed models will try to mark up beyond the base cost.
  - pheggs 6mo ago
    I personally believe that eventually manufacturers will want to sell more of their hardware and look for ways to sell hardware to consumers. isnt that situation quite similar to the days of early computers? I am for sure biased in hoping that will be the case
ponyous 6mo ago
Kimi is nowhere near GPT or Opus unfortunately. I really wish it was. I’m running evals where models have to generate code that produces 3D models and it’s obvious that it lacks spatial understanding and makes many more code errors before it succeeds. Maybe it’s better in one particular case here and there and I think this blog post is example of that.
- codedokode 6mo ago
  Not everyone needs 3D models to be fair.
- nmfisher 6mo ago
  Slightly OT, but after using DeepSeek V4 Pro for the last few weeks, I’ve found that it’s basically on par with Opus…except when it comes to driving Blender. This isn’t even a visual issue (DS isn’t multimodal), for whatever reason Opus just understands the Blender API a lot better. There always seem to be pockets where closed frontier models perform slightly better.
bazlightyear 6mo ago
BTW it looks like Kimi won the subsequent challenge too https://aicc.rayonnant.ai/challenges/hexquerques/ https://aicc.rayonnant.ai/challenges/hexquerques/
imrozim 6mo ago
Same experience here i use open router with claude as fallback for my startup. Is Kimi if close in quality the cost is difference hard to ignore
wg0 6mo ago
About 40% of stock market consists of about 7 or 8 companies. Those companies that are all into AI circular deals collectively trillions of dollars in valuations. Now imagine a company burning 200,000/month on AI spend. Real numbers. Not every company is but some are. Why such a company won't deploy an open weight model (Kimi 2.6 or Deepseek v4) on their own hardware (rented or otherwise) to save about 2.4 million dollars a year? And these are the landmines Chinese cleverly did set up. Not saying intentionally or otherwise. But end result is that good luck recouping your investments, you can pretty much kiss goodbye to any ROI. The bucket has a hole at the bottom and the bubble bust is guaranteed. PS: Without open weight models too the economics do not make sense neither the code generated by these SOTA models is reliable enough to be deployed as is. Anyone claiming otherwise either hasn't worked on a real software stack with real users OR didn't use AI long enough to witness the AI slop and how hard it is to untangle or de-slopify the AI generated code therefore these trillion dollar valuations are absurd anyway.
ant6n 6mo ago
What I would like to see is a comparison of how well the models work in long running conversations: * do they lie and gaslight * do they start breaking down on very long chats (forget old context, just get dumber) * do they constantly try to tell me how smart I am vs solving the problem (yes man) * do they follow conventions, parameters set out early in the prompts, or forget them * if they cant read a given file (like pdf), do they lie about it * is there a branch function to go back to earlier state of conversation * what is the quality of the presentation of results (structure, wording, excessive use of tables, appropriate use of headings) * how does the bot deal with user frustration (empathy?) For example Chatpgt 5.5 is fairly smart, but presentation of results is kind of poor and unstructured, and unnecessarily long. It will break down on long conversations (the long answers dont help here), and it can’t deal with that except lying and gaslighting. It also has very little empathy, and mostly ignores user frustration. But at least theres branching, so one can go back without completely starting over. Gemini doesnt feel quite as smart these days. It does well with very long conversations. Except it has bugs where all context gets lost or pruned, and it will lie and gaslight about it. Theres also no branching, so once context is lost you have to start over. Presentation is decent. Empathy is fairly good, except if users get frustrated, it gets more and more flustered and breaks down.
- Mashimo 6mo ago
  I think they all support branching if you use a agent like pi.
kmkrworks 6mo ago
I don't feel like this is an optimal way of comparing models. I really don't think any metric as of now has the ability to list down the best model as of now. It prioritizes tasks over the overall ability, and I don't even think it's possible to.
bjoli 6mo ago
As a musician, I find the butchering of musical notation on Kimis pricing page extremely off-putting.
yanis_t 6mo ago
Anecdotal, but having used Claude Code exclusively for the last several months, I was pleasantly surprised by how capable Pi + Kimi K2.6 is. It's also much faster (via OpenRouter), and at a fraction of the cost.
chillfox 6mo ago
Meanwhile, I can’t get kimi k2.6 to edit a heredoc in a shell script without it fucking it up.
zmmmmm 6mo ago
I've been switching across all different models this week with OpenCode and Pi - we're in an interesting place now because the open models are definitely "good enough" for a wide range of coding tasks and MUCH cheaper. They certainly aren't AS good, especially once you get into unfamiliar territory - custom enterprise frameworks etc where model knowledge falls off and general intelligence kicks in. But then, with time people will build up custom skills and agent files for those. And the open models will also get better. I could easily see us in a place 2 years from now where this coding application is fully commoditised.
muti 6mo ago
Doesn't seem like a very insightful result. Kimi won with the naive strategy. Other models didn't slide tiles at all or didn't demonstrate understanding of the rules, claiming words that lost points. A strategy that did nothing would beat them. We know these models can solve much more difficult problems, something isn't right.
tim0414 6mo ago
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alex7o 6mo ago
I don't know about you but kimi 2.6 from the kimi subscription has been absolutely bad and useless for the past 1 week so I canceled my sub and stopped using it.
warabe 6mo ago
I’m not trying to add fuel to the fire, but will OpenAI and Anthropic’s IPO go smoothly?
ibrahimhossain 6mo ago
[flagged]
gherkinnn 6mo ago
I never looked in to the details of these benchmarks, I live with the assumptions that most benchmarks of any kind are gamed and useless. What I do see in my own work and that of others around me, is that Claude consistently outperforms Gemini and to a lesser extent Codex. With Claude eating tokens with declining return, concessions have to be made and Codex is a usable middle ground. I use Kimi in Kagi's Assistant for non-code or generic programming questions and am quite happy with its no-bullshit responses.
_pdp_ 6mo ago
Kimi is capable model but it needs a very good harness. With a good harness it is a very capable model. But it can get into all kinds of issues (loops and such) something that frontier models do not. As I said, you can blame the model, but it is nothing that the harness cannot take care of more deterministically.
- Imustaskforhelp 6mo ago
  Which harness do you recommend using then for a model like kimi 2.6, Opencode or something else?
  - _pdp_ 6mo ago
    We have our own... so I was making the comment form our own experience working with the model. It is a lot trickier to use kimi compared to sonnet - hence why it seems that sonnet is more powerful while I think it is down to the harness.
    - Imustaskforhelp 6mo ago
      How did you make your own harness, I am curious to know more about the building process and please feel free to share your harness. If someone were to not use your harness and rather use some stock harness though, what is the one that you would recommend? I am curious about that too.
      - _pdp_ 6mo ago
        I really don't mean to discourage anyone but I find that making your own harness is pretty complex process. We where very naive when we started, thinking this is just a loop, but then it turned out it has all kinds of nuances, edge-cases, things to be considered, etc. As I am writing this I am fixing a bug in the harness that could cause infinite loops under some conditions. For simple interactions looping over the the llm complete function is not really that difficult. Put some tools, write the loop and exit. It starts getting more tricky when you need to detect cycling behaviour, when some tools might need to abort but do so gracefully, when you need to wait for something to complete while allowing other parts to continue, when you want to protect against too much usage after you hit certain thresholds, when you need to retry whatever, when you need to compact or truncate to maintain good context window and do so with the model in mind (kimi), when messages need to be structured in certain ways to handle various model capabilities, etc. Pi.dev, Hermes and all the other I have seen do not do even half of the things I have enumerated above - not at least out of the box.
ajdegol 6mo ago
I’ve been wondering about potential regression in coding models. The initial models were corrected by programmers which gave a very high quality feedback signal. Whereas with vibe coding on the rise, you’ll lose that signal.
codedokode 6mo ago
It's interesting that OpenAI promised to make AI accessible for everyone, but China actually did it.
adrian_b 6mo ago
> Xiaomi confirming that weights for their newer V2.5 Pro model are dropping soon This has already happened. I have downloaded both the big Pro model and the smaller but multimodal MiMo-V2.5. https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro https://huggingface.co/XiaomiMiMo/MiMo-V2.5 https://huggingface.co/XiaomiMiMo/MiMo-V2.5 https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base The download of MiMo-V2.5-Pro takes 963 GB, while that of MiMo-V2.5 takes 295 GB. For comparison, the download of Kimi-K2.6 takes 555 GB.
PunchyHamster 6mo ago
That is not a programming challenge, the fuck
syntex 6mo ago
These benchmarks means very little. The real test is model + harness so agentic system that can fulfill given goals.
VeejayRampay 6mo ago
crazy how people on hacker news, who just gobble up anything if it's from openai or anthropic suddenly become monocled sceptics when chinese open models are "winning"
gizmodo59 6mo ago
“I did not wake up to be a loser. This loser attitude, makes no sense to me.” - Frontier Model Labs (original quote by Jensen in a podcast)
childintime 6mo ago
Is there a lo-slop model that stands out when using Zig?
- zozbot234 6mo ago
  Zig changes way too fast for AI to be generally good at it. Have it generate Rust instead.
raffael_de 6mo ago
I find this practically relevant insofar K2.6 is offered by Kagi in the 2 lower priced plans for $5 and $10 per month.
prirun 6mo ago
I don't know much about the AI field, but it seems to me that trying to train any model to be all things to all people is a really dumb idea. It requires huge financial resources and is causing extreme shortages/market distortions in any resource used by an AI company - RAM, SSDs, data centers, etc. In the real world, you don't hire a plumber and expect him to also do your landscaping, fix your car, and tailor your clothes. It would seem like a much better use of resources if I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp. As humans, we've done pretty well by specializing. I hope this gets explored more with smaller, focused AI models vs the current path of one model to rule them all that can only be run in a data center the size of a country.
- davidguetta 6mo ago
  This is true unless it isn't basically. People claim that finetuning is good because no model can be 'that' general since gpt3 and at every generation it's becoming less true
- ElectricalUnion 6mo ago
  > I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp. I would daresay for "coding tasks", you actually _want_ a model that can code "in all languages". Sure, it might be that outdated language XYZ is really useless to you or the task you want, but being exposed to their limits, philosophy and concerns across environment, framework and organization, among other things, means for example you get insights of your problems from other areas and points of view. That's afterall how we got Newtonian physics and calculus, right? A person studying physics someday noticed how the "math of the day" wasn't able to calculate some results without a lot of elbow grease. He then "found" the "missing math" and with it was able to generalize what at the time was considered a bunch of isolated phenomena into a cohesive corpus of knowledge. So for example, I want my code to have mechanical sympathy like Fortran; well defined input/output interfaces, and not-interweaved control structures, like COBOL; stateless, side-effects-free business logic like Lisp.
deleted 6mo ago
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pllbnk 6mo ago
What would prevent Anthropic or Open AI take one of the open source models, tune it to "feel" like one of their models and expose them under their respective names (GPT or Claude) to save on training and inference, but keep their brand name's popularity? If the models are not objectively worse and are cheaper to run, then nobody would complain, or complaining baseline would stay the same.
noashavit 6mo ago
Why are we still discussing which model is “the best”. The model is just one small part of what you need. Think agentic harness, data governance, AI guardrails, machine access controls, etc
kk_mors 6mo ago
[flagged]

380 points by bazlightyear 6mo ago

In a single challenge, measured by how performant the solution was. Kimi K2.6 is definitely a frontier-sized model, so on the one hand it's not that surprising it's up there with the closed frontier models. Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU.

echelon 6mo ago
This is the future though. Open weights models that run on H200s provide far more opportunity to build products and real infrastructure around. You can always distill this for your little RTX at home. But models shaped for consumer hardware will never win wide adoption or remain competitive with frontier labs. This is something that _can_ compete. And it will both necessitate and inspire a new generation of open cloud infra to run inference. "Push button, deploy" or "Push button, fine tune" shaped products at the start, then far more advanced products that only open weights not locked behind an API can accomplish. Now we just need open weights Nano Banana Pro / GPT Image 2, and Seedance 2.0 equivalents. The battle and focus should be on open weights for the data center.
- bitmasher9 6mo ago
  I don’t fully understand what open weights unlocks that cannot be accomplished via API from a product standpoint. Open weights is great if you want to do additional training, or if you need on-prem for security.
  - stldev 6mo ago
    Or try to beat Anthropic's uptime.
  - mkl 6mo ago
    Multiple providers of the same model. That means competition for price, reliability, latency, etc. It also means you can use the same model as long as you want, instead of having it silently change behaviour.
    - Bombthecat 6mo ago
      Those open weight providers where found nerfing models too.
  - echelon 6mo ago
    > Open weights is great if you want to do additional training, or if you need on-prem for security. The power of giving universities, companies, and hackers "full" models should not be understated. Here are a just a few ideas for image, video, and creative media models: - Suddenly you're not "blocked" for entire innocuous prompts. This is a huge issue. - You can fine tune the model to learn/do new things. A lighting adjustment model, a pose adjustment model. You can hook up the model to mocap, train it to generate plates, etc. - You can fine tune it on your brand aesthetic and not have it washed out.
- zozbot234 6mo ago
  These large MoE models can work quite well on consumer or prosumer platforms, they'll just be slow, and you have to offset that by running them unattended around the clock. (Something that you can't really do with large SOTA models without spending way too much on tokens.) This actually works quite well for DeepSeek V4 series which has comparatively tiny KV-cache sizes so even a consumer platform can run big batches in parallel.
joshoink 6mo ago
[flagged]
keyle 6mo ago
It absolutely does matter. The enshittification will go unnoticed at first but I'm already finding my favourite frontier models severely nerfed, doing incredibly dumb stuff they weren't in the past. We need open weight models to have a stable "platform" when we rely on them, which we do more and more.
- magicalhippo 6mo ago
  Most people won't roll out their own K2 deployment across rented GPUs, so in that sense it doesn't matter that much, they'll be using a paid service which is just as much of a black box as Claude or ChatGPT. For example, on OpenRouter you can select a provider which state they use a given open model, but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on. That said, I do fully agree that it is valuable to have open near-frontier models, as a balance to the closed ones.
  - slopinthebag 6mo ago
    It's not really a black box. Useful models becoming fungible is crucial for disincentivizing bad behaviour with model providers. I can't really overstate how different it is from relying on closed models. If you don't like or trust any of the providers on OpenRouter you can rent the GPUs yourself and host it, although this is probably unnecessary.
  - roenxi 6mo ago
    > but you have no idea what actually goes on behind the curtains, which quantization levels they use and so on. That would take something close to a global conspiracy of every technologist lying continuously to keep the tweaks secret. If necessary, I personally will rent some servers and run a vanilla Kimi K2.6 deployment for people to use at reasonable prices. I don't expect to ever make good on that threat because they are grim times indeed if I'm the first person doing something AI related, but the skill level required to load up a model behind an API is low. So it isn't hard to see how there will be unadulterated Kimi models available and from there it is really, really straightfoward to tell if someone is quantising a model; just run some benchmarks against 2 different providers who both claim to serve the same thing. If one is quantising and another isn't there's a big difference in quality.
DeathArrow 6mo ago
>Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU. Of course it matters because that makes coding plans much cheaper than those from Anthropic and OpenAI. For personal use I have coding plans with GLM 5.1, Kimi K2.6, MiniMax M2.7 and Xiaomi MiMo V2.5 Pro and I am getting a lot of bang for the buck.
- magicalhippo 6mo ago
  Currently it's not a huge difference given the subsidies of closed model subscriptions. Once that stops then yea it will be really nice to have open models as price competitors.
  - smj-edison 6mo ago
    At least in my experience switching from Claude Pro ($20/month) to Kimi 2.6 through ollama (also $20/month), I was almost always hitting my usage limit with Sonnet 4.6, but with ollama I haven't hit my usage a single time.
    - ode 6mo ago
      How many t/ps do you get with Kimi on Ollama?
      - smj-edison 6mo ago
        I actually have no clue how I'd check, do you know how?
  - DeathArrow 6mo ago
    >Currently it's not a huge difference given the subsidies of closed model subscriptions. With Claude Max I was hitting the limits very fast.
lelanthran 6mo ago
> Being open is nice though, even though it doesn't matter that much for folks like me with a single consumer GPU. The value of open source is not that you will run it locally, it's that anyone can run it at all. Even if you can't afford to purchase the hardware to run large open source models, someone would, price it at half the cost of the closed source models and still make a profit. The only reason you are not seeing that happen right now is because the current front-running token-providers have subsidised their inference costs. The minute they start their enshittification the market for alternatives becomes viable. Without open-source models, there will never be a viable alternative. Even if they wanted to charge only 80% of what a developer costs, the existence of open source models that are not far behind is a forcing function on them. There is no moat for them.
- 0xkvyb 6mo ago
  Totally agree with you. There is only so much time before SF tech runs out of subsidy bucks, and Chinese models take the consumer spotlight
- yorwba 6mo ago
  The reason nobody is pricing Kimi K2.6 at half the cost of the best closed source models is that there are too many providers of the same model, so the competition drives prices down and they have to charge far less than that. https://openrouter.ai/moonshotai/kimi-k2.6/providers https://openrouter.ai/moonshotai/kimi-k2.6/providers
tom2026hn 6mo ago
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PedroBatista 6mo ago

Great to know, but what was the cost both in terms of $$ and tokens used? Not to invalidate these benchmark results because they are useful, but the real usefulness it what they are capable to do when real people interact with them at scale. Regardless, these are good news, because now that Microsoft is basically giving up their all-in strategy with Github's Copilot and Anthropic is playing the "I'm too good for you" game, it's about time for them to get pressed into not making this AI world into a divide between the haves and the have-nots.

keyle 6mo ago
Re pricing. Never as high as frontier commercial models.
- CryptoBanker 6mo ago
  You’d be surprised with some long running complex tasks. I’ve seen Kimi spend 8 minutes (total) thinking on a task that Claude got done in 30 seconds. They both ultimately got it right, but Kimi spent ~$2.25 to Claude’s ~$0.20

beering 6mo ago

I’m a little confused as to the setup. It was asking each model to one-shot a script and then the scripts faced off? Were the models given a computer environment? Or a test server to iterate against?

rpmisms 6mo ago
Sounds incredibly simple to me. One-shot.
- beering 6mo ago
  So nothing like real-world coding, where you’d be able to run and test the script before submitting?
  - procinct 6mo ago
    One shot just means the user doesn’t have to iterate on it via the agent. The agent does what ever it needs to deliver the best outcome, including its own running and iteration until it’s happy with it. This could be a short or long process potentially depending on the task.

Frannky 6mo ago

I have to try Kimi. I was looking for an alternative. If you have any experience, advice, please share. I saw Kimi is at the top of the Open Router ranking.

zorked 6mo ago
I use Kimi at home via a kimi.com subscription and Kimi CLI (sometimes running inside Zed, sometimes not). My favorite model by far. And it's just $20. I have to use a supposedly frontier model at work and I hate it.
- Frannky 6mo ago
  Nice, thanks for sharing!
DeathArrow 6mo ago
Kimi K2.6 is great but I advice you to get a coding plan from Kimi.com as that way is much cheaper than paying for API calls using OpenRouter.
- Frannky 6mo ago
  Thanks, I am trying it right now. I had an opencode plan 5$/month, so I will play with that. I use ZED and I added Pi ACP, so I can try the both pi and Kimi. I will also try it in opencode and via Kimi code.
  - prvnsmpth 6mo ago
    Use kimi 2.6 for planning and a cheap model (preferably local) for execution, and then kimi once again for reviewing it. Then finally I review the code. Saves a lot on tokens.
    - Frannky 6mo ago
      Very interesting, thanks for sharing. I am testing it with Pi in Zed and it seems pretty good.

elromulous 6mo ago

Is the site just slashdotted rn? Can anyone get to it?

brettgo1 6mo ago
Slashdot... Now that's a name I haven't heard in a long time. A long time.

deleted 6mo ago

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surrTurr 6mo ago

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jakemanger 6mo ago

What's the GPU VRAM requirements for this thing? Awesome to have a open model that can compete, but damn it would be so much better if you could run it locally. Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc

DeathArrow 6mo ago
>Otherwise, it's almost so difficult to run (e.g. self host) that it's just way more convenient to pay OpenAI, Claude, etc Getting a coding plan from Kimi.com will make coding 20x cheaper than using Anthropic. BTW, I am using it with Claude Code.
redrove 6mo ago
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slashdave 6mo ago

I was surprised by the ranking, until I read what the test was. Not horribly relevant for coding. The current ranking of all tests makes more sense (well, except for how well Gemini does) https://aicc.rayonnant.ai https://aicc.rayonnant.ai

mpeg 6mo ago
If you look at the ranking breakdown though, Kimi K2.6 has only participated in the last 5 challenges (claude dominated before then) and if you only count those it would be in first place
- Sammi 6mo ago
  It also has a DNF. So it has a high ceiling but also unfortunately a low floor. So using Kimi means accepting high variability of the output. Personally what I've found that has made coding agents more and more useful over the last year is that they have gotten a higher and higher floor, not that they have gotten a higher and higher ceiling. They were already plenty smart a year ago, it was just that they failed so often and so spectacularly that it made them a liability. Now they have become much more reliable, which is the key thing that has transitioned them into being actually useful. For the most part I don't use them to work on really intellectually difficult tasks. I mostly use them to work on very boring and labor intensive tasks. Most commercial software development work is just boring drudgery like this. Certainly the bulk of what I need them for is. I need them to just not crap their pants all the time while they're at it. So I'm kinda wary seeing the poor reliability of Kimi.
  - mpeg 6mo ago
    If you look at the last 5 challenges (the ones Kimi was in) both Claude and Kimi have 1 DNF, chatgpt has 2 I'm not sure this is enough data to form an opinion, but going by what we have Kimi would be as reliable as Claude
SeriousM 6mo ago
The ranking of gold medals only makes sense if all models would gave participate all tests. DNP = Did not participate In this regard, kimi got more and better medals than Claude.

pbreit 6mo ago

All my co-workers say Claude blows away Gemini. Is it really that good? How can I do Kimi?

prvnsmpth 6mo ago
You can sign up for a plan on the kimi code platform and use it via the pi.dev coding agent, or opencode. In planning, I’d say it’s almost on par with Claude Opus.

Rekindle8090 6mo ago

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justech 6mo ago

I’ve been maining Kimi k2.6 through opencode go and openrouter for a week and I can say it’s the same experience as when I was maining Sonnet 3.5/4 late last year. Not as good or as fast as Claude Code on Opus now but definitely enough for casual/hobby use. The best part is multiple choices for providers, if opencode gimps their service, I’ll switch

jrecyclebin 6mo ago

I absolutely love Kimi's personality - some of the things it says are so out there! And it's been great for very focused, iterative work. Its weakness is that it seems to yak on-and-on when it needs to plan out something big or read through and make sense of how to use a niche piece of a complex library. To the point where it can fill up its 256k window - and rack up a build. (No cache.) I have had better experience with GLM 5.1 in those cases. Anyone out there relate?

anderber 6mo ago
Absolutely. I use caveman to help with that: https://github.com/JuliusBrussee/caveman https://github.com/JuliusBrussee/caveman
- jrecyclebin 6mo ago
  Not a bad idea - however > Caveman only affects output tokens — thinking/reasoning tokens are untouched. The problem is the thinking. But could help to tune my system prompt for Kimi.
- LeoPanthera 6mo ago
  You can just add "be brief" to the prompt to replace the entire plugin. Same results. https://www.maxtaylor.me/articles/i-benchmarked-caveman-against-two-words https://www.maxtaylor.me/articles/i-benchmarked-caveman-agai...

gertlabs 6mo ago

I'm glad we're seeing a shift towards objectively scored tests. We've been doing this at scale at https://gertlabs.com/rankings https://gertlabs.com/rankings, and although the author looks to be running unique one-off samples, it's not surprising to see how well Kimi K2.6 performed. Based on our testing, for coding especially, Kimi is within statistical uncertainty of MiMo V2.5 Pro for top open weights model, and performs much better with tools than DeepSeek V4 Pro. GPT 5.5 has a comfortable lead, but Kimi is on par with or better than Opus 4.6. The problem with Kimi 2.6 is that it's one of the slower models we've tested.

veber-alex 6mo ago
In my experience benchmarks are pretty meaningless. Not only is performance dependent on the language and tasks gives but also the prompts used and the expected results. In my own internal tests it was really hard to judge whether GPT 5.5 or Opus 4.7 is the better model. They have different styles and it's basically up to preference. There where even times where I gave the win to one model only to think about it more and change my mind. At the end of the day I think I slightly prefer Opus 4.7.
- gertlabs 6mo ago
  I think benchmarks are improving and will always have value, but it's the equivalent to someone's college and GPA for an entry level job application. It's a strong signal for a job, but the soft skills are sometimes going to get Claude Opus 4.6 a job over smarter applicants. That's what we'd really like to measure objectively, and are actively working on.
- magicalhippo 6mo ago
  In addition, the harness around these models do a lot of work and changes the outcome significantly. I just had an issue where Claude CLI with Opus 4.7 High could not figure out why my Blazor Server program was inert, buttons didn't do anything etc. After several rounds, I opened the web console and found that it failed to load blazor.js due to 404 on that file. I copied the error message to Claude CLI and after another several unproductive rounds I gave up. I then moved the Codex, with ChatGTP 5.5 High. I gave it the code base, problem description and error codes. Unlike Claude CLI it spun up the project and used wget/curl to probe for blazor.js, and found indeed it was not served. It then did a lot more probing and some web searches and after a while found my project file was missing a setting. It added that and then probed to verify it worked. So Codex fixed it in about 20 minutes without me laying hands on it (other than approve some program executions). However, I'm not convinced this shows GPT 5.5 being that much better than Opus 4.7. It could very well be the harness around it, the system prompts used in the harness and tools available. For reference this was me just trying to see how good the vibecoding experience is now, so was trying to do this as much hands-off as possible.

SomaticPirate 6mo ago

This seems to be testing the models on leetcode style prompts that also require the model to implement TCP calls to send the results. Interesting but probably not a apples to apples comparison. The fact only Grok qualified for the first one seems suspect

aykutseker 6mo ago

This seems less like Kimi is better at coding than Claude and more like Kimi found the right strategy for this particular game. Still interesting though. The fact that an open weight model is close enough for that to matter is probably the real story.

rvz 6mo ago

So we are now at the point where open weight models are rapidly catching up to the frontier models. They are at best 30 days behind, and at worst case 2 months behind. The last issue is being able to run the best one on conventional hardware without a rack of GPUs. The Macbooks, and Mac minis are behind on hardware but eventually in the next 2 years at worst will make it possible thanks to the advancements of the M-series machines. All of this is why companies like Anthropic feel like they have to use "safety" to stop you from running local models on your machine and get you hooked on their casino wasting tokens with a slot machine named Claude.

qakajjqj 6mo ago

Yes gimini is a programming application

0xbadcafebee 6mo ago

These posts are going to be a constant for the next year, because there's no objective way to compare models (past low-level numbers like token generation speed, average reasoning token amount, # of parameters, active experts, etc). They're all quite different in a lot of ways, they're used for many different things by different people, and they're not deterministic. So you're constantly gonna see benchmarks and tests and proclamations of "THIS model beat THAT model!", with people racing around trying to find the best one. But there is no best one. There's just the best one for you, based on whatever your criteria is. It's likely we'll end up in a "Windows vs MacOS vs Linux" style world, where people stick to their camps that do a particular thing a particular way.

verve_rat 6mo ago
My theory is we will end up in a similar spot to hiring people. You can look at a CV (benchmarks) but you won't know for sure until you've worked with them for six months. We as an industry cannot determine if one software engineer is objectively better than another, on practically any dimension, so why do we think we can come to an objective ranking of models?
- zelphirkalt 6mo ago
  Not many things are as manifold broken as hiring these days. I hope we do not end up there.
  - deleted 6mo ago
    [deleted]
- pishpash 6mo ago
  You do not interview 1000 rounds on problems you're actually solving. If you did, hiring would be fine. Minus the social fit aspect, which isn't as relevant for a model.
- roymain 6mo ago
  The CV-to-six-months analogy is actually exactly right and it's also why benchmarks for hiring people stopped being useful. The signal that holds up is what you see when something breaks, which is hard to compress into a number.

slopinthebag 6mo ago

Amazing. To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Truly a great time to be alive. I would like to see more effort making the flash variants work for coding. They are super economical to use to brute force boilerplate and drudgery, and I wonder just how good they can be with the right harness, if it provides the right UX for the steering they require. As much as vibe coding has captured the zeitgeist, I think long term using them as tools to generate code at the hands of skilled developers makes more sense. Companies can only go so long spending obscene amounts of money for subpar unmaintainable code.

Barbing 6mo ago
>To me it feels like GLM 5.1, Kimi 2.6, DeepSeek 4 are all competitive both with each other and with the American models. Yes, at least probably with each other >Truly a great time to be alive. Do wish outcome had been better for those whose work ended up in the training sets, wish competition could’ve found ways to agree on distillation practices, wish globally we’d planned as fast as we’re developing… Tremendous excitement too

walrus01 6mo ago

People thinking to self-host Kimi K2.6 had better be prepared for how big it is. Q8 K XL quantization for instance is around 600GB on disk. I would bet about 700GB of VRAM needed. Quantizations lower than Q8 are probably worthless for quality. Or 2.05TB on disk for the full precision GGUF. https://huggingface.co/unsloth/Kimi-K2.6-GGUF https://huggingface.co/unsloth/Kimi-K2.6-GGUF If you can afford the hardware to run Kimi K2.6 at any decent speed for more than 1 simultaneous user, you probably have a whole team of people on staff who are already very familiar with how to benchmark it vs Claude, GPT-5.5, etc.

zozbot234 6mo ago
Kimi is a natively quantized model, the lossless full precision release is 595GB. Your own link mentions that.
- CamperBob2 6mo ago
  So, realistically, $100K for an 8x RTX 6000 Pro system that can run it at a usable rate.
  - zozbot234 6mo ago
    I think people will always disagree on what qualifies as a "usable rate". But keep in mind that practically no one sensible is running the latest Opus or GPT around the clock, especially not at sustainable, unsubsidized prices. With open-weights models it's easy to do that.
    - walrus01 6mo ago
      Also for people doing something medical, privacy or sensitive data related, there's an almost incalculable value (depending on industry niche) in having absolutely no external network traffic to any servers/systems you don't fully control.
- walrus01 6mo ago
  the 'unsloth' link above is a 3rd party person that has quantized it to Q8, the original release is considerably larger in size than 600GB: https://huggingface.co/moonshotai/Kimi-K2.6 https://huggingface.co/moonshotai/Kimi-K2.6

sieve 6mo ago

Kimi is really good. I have been using Sonnet and others (DeepSeek, ChatGPT, MiniMax, Qwen) for my compiler/vm project and the Claude Pro plan is mostly unusable for any serious coding effort. So I use it in chat mode in the browser where it cannot needlessly read your entire project, and use Kimi on the OpenCode Go plan with pi. Kimi consistently exceeded Sonnet on the C+Python project. Never had to worry about it doing anything other than what I asked it to do. GLM crapped the bed once or twice. Kimi never did.

joe_mamba 6mo ago
>the Claude Pro plan is mostly unusable for any serious coding effort Why? Seems to go a giant the opinion of the masses who mostly use Claude Pro for serious coding.
- atraac 6mo ago
  Anecdotal evidence but last week or two Claude changed something related to their quotas. I'm a Pro user(now Team Standard) and while I did quite a lot before with that subscription, past week the 5h quota quite literally lasts maybe 5 semi sized prompts. I don't "vibe" anything, I give it clearly defined tasks or things to debug/fix, nothing hardcore. I ran out of the quota every single day past week, often twice a day, this never happened before. It's rather unusable for actual professional usage now. I'm tempted to test Codex over next week to compare hence why we're waiting with going to Claude Max sub.
- LUmBULtERA 6mo ago
  I think ths comment is referring to the usage limits of the Pro plan. You run out of usage very fast if on anything less than a Max ($100+/month) plan.
- sieve 6mo ago
  Claude is opaque as regards token usage. So you might end up using your 5hr limit in 7-10 minutes using regular Sonnet. Meanwhile, OpenCode etc give you exact breakdown in terms of how many cached tokens used per session etc which you can use to estimate burn rate. All these coding tools are extremely wasteful as far as resources are concerned. Almost designed to make you move to the next tier. You have to consciously restrict their scope all the time to make your plans last. Even with Kimi/MiniMax a 3-4 hour session often ends up with 50-70M cached reads. Not a small amount at all.

plexescor 6mo ago

I always though claude is the goat, but i guess its time to change the notion and try Kimi K2.6

koala-news 6mo ago

In my opinion, this kind of comparison is not very meaningful.

ninjahawk1 6mo ago

At the current rate, open sourced models are expected to surpass cloud models within a couple years based on a study I read a couple days ago. Looking back at chatGPT and claude a couple years ago, very small Qwen models are basically equal in coding to what those cloud based models could do then. Also factoring in scaling laws, a 9b going to 18b is roughly a 40% increase, whereas 18b to 35b is 20%, I expect there will be a change of at least price in cloud based models. Adobe used to be $600 per month, then it became $20 when distribution scaled.

baxtr 6mo ago
While this might be true I’m worried about the hardware side of things. What if you have a good enough model but the cloud model providers are better in procuring hardware for interference?
- gleenn 6mo ago
  Local inference is definitely going to make more and more sense. Modern CPUs have all this amazing hardware well-optimized for inference purposes. I use a lot of web tools and see AI baked in and it feels weird. I want the smartness localized for speed and data security. I think and hope the industry points towards smart ai agents operating as locally as possible.
- zozbot234 6mo ago
  The cloud providers are probably better at procuring hardware for inference, but on prem users are better at repurposing hardware that they'd need anyway for their existing uses. In a world where AI compute is likely inherently scarce, it makes sense to rely on both.
- Gigachad 6mo ago
  You’ll be able to run the open models on any cloud at the cost of the hardware rental. While the closed models will try to mark up beyond the base cost.
- pheggs 6mo ago
  I personally believe that eventually manufacturers will want to sell more of their hardware and look for ways to sell hardware to consumers. isnt that situation quite similar to the days of early computers? I am for sure biased in hoping that will be the case

ponyous 6mo ago

Kimi is nowhere near GPT or Opus unfortunately. I really wish it was. I’m running evals where models have to generate code that produces 3D models and it’s obvious that it lacks spatial understanding and makes many more code errors before it succeeds. Maybe it’s better in one particular case here and there and I think this blog post is example of that.

codedokode 6mo ago
Not everyone needs 3D models to be fair.
nmfisher 6mo ago
Slightly OT, but after using DeepSeek V4 Pro for the last few weeks, I’ve found that it’s basically on par with Opus…except when it comes to driving Blender. This isn’t even a visual issue (DS isn’t multimodal), for whatever reason Opus just understands the Blender API a lot better. There always seem to be pockets where closed frontier models perform slightly better.

bazlightyear 6mo ago

BTW it looks like Kimi won the subsequent challenge too https://aicc.rayonnant.ai/challenges/hexquerques/ https://aicc.rayonnant.ai/challenges/hexquerques/

imrozim 6mo ago

Same experience here i use open router with claude as fallback for my startup. Is Kimi if close in quality the cost is difference hard to ignore

wg0 6mo ago

About 40% of stock market consists of about 7 or 8 companies. Those companies that are all into AI circular deals collectively trillions of dollars in valuations. Now imagine a company burning 200,000/month on AI spend. Real numbers. Not every company is but some are. Why such a company won't deploy an open weight model (Kimi 2.6 or Deepseek v4) on their own hardware (rented or otherwise) to save about 2.4 million dollars a year? And these are the landmines Chinese cleverly did set up. Not saying intentionally or otherwise. But end result is that good luck recouping your investments, you can pretty much kiss goodbye to any ROI. The bucket has a hole at the bottom and the bubble bust is guaranteed. PS: Without open weight models too the economics do not make sense neither the code generated by these SOTA models is reliable enough to be deployed as is. Anyone claiming otherwise either hasn't worked on a real software stack with real users OR didn't use AI long enough to witness the AI slop and how hard it is to untangle or de-slopify the AI generated code therefore these trillion dollar valuations are absurd anyway.

ant6n 6mo ago

What I would like to see is a comparison of how well the models work in long running conversations: * do they lie and gaslight * do they start breaking down on very long chats (forget old context, just get dumber) * do they constantly try to tell me how smart I am vs solving the problem (yes man) * do they follow conventions, parameters set out early in the prompts, or forget them * if they cant read a given file (like pdf), do they lie about it * is there a branch function to go back to earlier state of conversation * what is the quality of the presentation of results (structure, wording, excessive use of tables, appropriate use of headings) * how does the bot deal with user frustration (empathy?) For example Chatpgt 5.5 is fairly smart, but presentation of results is kind of poor and unstructured, and unnecessarily long. It will break down on long conversations (the long answers dont help here), and it can’t deal with that except lying and gaslighting. It also has very little empathy, and mostly ignores user frustration. But at least theres branching, so one can go back without completely starting over. Gemini doesnt feel quite as smart these days. It does well with very long conversations. Except it has bugs where all context gets lost or pruned, and it will lie and gaslight about it. Theres also no branching, so once context is lost you have to start over. Presentation is decent. Empathy is fairly good, except if users get frustrated, it gets more and more flustered and breaks down.

Mashimo 6mo ago
I think they all support branching if you use a agent like pi.

kmkrworks 6mo ago

I don't feel like this is an optimal way of comparing models. I really don't think any metric as of now has the ability to list down the best model as of now. It prioritizes tasks over the overall ability, and I don't even think it's possible to.

bjoli 6mo ago

As a musician, I find the butchering of musical notation on Kimis pricing page extremely off-putting.

yanis_t 6mo ago

Anecdotal, but having used Claude Code exclusively for the last several months, I was pleasantly surprised by how capable Pi + Kimi K2.6 is. It's also much faster (via OpenRouter), and at a fraction of the cost.

chillfox 6mo ago

Meanwhile, I can’t get kimi k2.6 to edit a heredoc in a shell script without it fucking it up.

zmmmmm 6mo ago

I've been switching across all different models this week with OpenCode and Pi - we're in an interesting place now because the open models are definitely "good enough" for a wide range of coding tasks and MUCH cheaper. They certainly aren't AS good, especially once you get into unfamiliar territory - custom enterprise frameworks etc where model knowledge falls off and general intelligence kicks in. But then, with time people will build up custom skills and agent files for those. And the open models will also get better. I could easily see us in a place 2 years from now where this coding application is fully commoditised.

muti 6mo ago

Doesn't seem like a very insightful result. Kimi won with the naive strategy. Other models didn't slide tiles at all or didn't demonstrate understanding of the rules, claiming words that lost points. A strategy that did nothing would beat them. We know these models can solve much more difficult problems, something isn't right.

tim0414 6mo ago

[dead]

alex7o 6mo ago

I don't know about you but kimi 2.6 from the kimi subscription has been absolutely bad and useless for the past 1 week so I canceled my sub and stopped using it.

warabe 6mo ago

I’m not trying to add fuel to the fire, but will OpenAI and Anthropic’s IPO go smoothly?

ibrahimhossain 6mo ago

[flagged]

gherkinnn 6mo ago

I never looked in to the details of these benchmarks, I live with the assumptions that most benchmarks of any kind are gamed and useless. What I do see in my own work and that of others around me, is that Claude consistently outperforms Gemini and to a lesser extent Codex. With Claude eating tokens with declining return, concessions have to be made and Codex is a usable middle ground. I use Kimi in Kagi's Assistant for non-code or generic programming questions and am quite happy with its no-bullshit responses.

_pdp_ 6mo ago

Kimi is capable model but it needs a very good harness. With a good harness it is a very capable model. But it can get into all kinds of issues (loops and such) something that frontier models do not. As I said, you can blame the model, but it is nothing that the harness cannot take care of more deterministically.

Imustaskforhelp 6mo ago
Which harness do you recommend using then for a model like kimi 2.6, Opencode or something else?
- _pdp_ 6mo ago
  We have our own... so I was making the comment form our own experience working with the model. It is a lot trickier to use kimi compared to sonnet - hence why it seems that sonnet is more powerful while I think it is down to the harness.
  - Imustaskforhelp 6mo ago
    How did you make your own harness, I am curious to know more about the building process and please feel free to share your harness. If someone were to not use your harness and rather use some stock harness though, what is the one that you would recommend? I am curious about that too.
    - _pdp_ 6mo ago
      I really don't mean to discourage anyone but I find that making your own harness is pretty complex process. We where very naive when we started, thinking this is just a loop, but then it turned out it has all kinds of nuances, edge-cases, things to be considered, etc. As I am writing this I am fixing a bug in the harness that could cause infinite loops under some conditions. For simple interactions looping over the the llm complete function is not really that difficult. Put some tools, write the loop and exit. It starts getting more tricky when you need to detect cycling behaviour, when some tools might need to abort but do so gracefully, when you need to wait for something to complete while allowing other parts to continue, when you want to protect against too much usage after you hit certain thresholds, when you need to retry whatever, when you need to compact or truncate to maintain good context window and do so with the model in mind (kimi), when messages need to be structured in certain ways to handle various model capabilities, etc. Pi.dev, Hermes and all the other I have seen do not do even half of the things I have enumerated above - not at least out of the box.

ajdegol 6mo ago

I’ve been wondering about potential regression in coding models. The initial models were corrected by programmers which gave a very high quality feedback signal. Whereas with vibe coding on the rise, you’ll lose that signal.

codedokode 6mo ago

It's interesting that OpenAI promised to make AI accessible for everyone, but China actually did it.

adrian_b 6mo ago

> Xiaomi confirming that weights for their newer V2.5 Pro model are dropping soon This has already happened. I have downloaded both the big Pro model and the smaller but multimodal MiMo-V2.5. https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro https://huggingface.co/XiaomiMiMo/MiMo-V2.5 https://huggingface.co/XiaomiMiMo/MiMo-V2.5 https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base The download of MiMo-V2.5-Pro takes 963 GB, while that of MiMo-V2.5 takes 295 GB. For comparison, the download of Kimi-K2.6 takes 555 GB.

PunchyHamster 6mo ago

That is not a programming challenge, the fuck

syntex 6mo ago

These benchmarks means very little. The real test is model + harness so agentic system that can fulfill given goals.

VeejayRampay 6mo ago

crazy how people on hacker news, who just gobble up anything if it's from openai or anthropic suddenly become monocled sceptics when chinese open models are "winning"

gizmodo59 6mo ago

“I did not wake up to be a loser. This loser attitude, makes no sense to me.” - Frontier Model Labs (original quote by Jensen in a podcast)

childintime 6mo ago

Is there a lo-slop model that stands out when using Zig?

zozbot234 6mo ago
Zig changes way too fast for AI to be generally good at it. Have it generate Rust instead.

raffael_de 6mo ago

I find this practically relevant insofar K2.6 is offered by Kagi in the 2 lower priced plans for $5 and $10 per month.

prirun 6mo ago

I don't know much about the AI field, but it seems to me that trying to train any model to be all things to all people is a really dumb idea. It requires huge financial resources and is causing extreme shortages/market distortions in any resource used by an AI company - RAM, SSDs, data centers, etc. In the real world, you don't hire a plumber and expect him to also do your landscaping, fix your car, and tailor your clothes. It would seem like a much better use of resources if I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp. As humans, we've done pretty well by specializing. I hope this gets explored more with smaller, focused AI models vs the current path of one model to rule them all that can only be run in a data center the size of a country.

davidguetta 6mo ago
This is true unless it isn't basically. People claim that finetuning is good because no model can be 'that' general since gpt3 and at every generation it's becoming less true
ElectricalUnion 6mo ago
> I could download an app that specialized in shell, Python, and C coding for example, or maybe even that would be 3 apps that communicated. Maybe I could even run them on a regular machine with 16GB of RAM. I don't need one huge model that can do that and code in Fortran, COBOL, and Lisp. I would daresay for "coding tasks", you actually _want_ a model that can code "in all languages". Sure, it might be that outdated language XYZ is really useless to you or the task you want, but being exposed to their limits, philosophy and concerns across environment, framework and organization, among other things, means for example you get insights of your problems from other areas and points of view. That's afterall how we got Newtonian physics and calculus, right? A person studying physics someday noticed how the "math of the day" wasn't able to calculate some results without a lot of elbow grease. He then "found" the "missing math" and with it was able to generalize what at the time was considered a bunch of isolated phenomena into a cohesive corpus of knowledge. So for example, I want my code to have mechanical sympathy like Fortran; well defined input/output interfaces, and not-interweaved control structures, like COBOL; stateless, side-effects-free business logic like Lisp.

deleted 6mo ago

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pllbnk 6mo ago

What would prevent Anthropic or Open AI take one of the open source models, tune it to "feel" like one of their models and expose them under their respective names (GPT or Claude) to save on training and inference, but keep their brand name's popularity? If the models are not objectively worse and are cheaper to run, then nobody would complain, or complaining baseline would stay the same.

noashavit 6mo ago

Why are we still discussing which model is “the best”. The model is just one small part of what you need. Think agentic harness, data governance, AI guardrails, machine access controls, etc

kk_mors 6mo ago

[flagged]