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Kimi K3 Is Competitive with Fable; Kimi K3 and Fable Is SoTA
(fireworks.ai)
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by piotrgrabowski
Also: Kimi K3: second only to Fable 5 on AA-Briefcase
https://artificialanalysis.ai/articles/kimi-k3-agentic-knowl...
by piotrgrabowski
However they do seem very close to SOTA : I’d say roughly equal to previous gen (Opus 4.8, GPT 5.5). It’s yet another silly benchmark, but compare them here: https://senko.net/vibecode-bench/
I also had K3, Qwen3.8 and Fable (using Kimi Code, Qwen Code and Claude Code harnesses respectively, and the official APIs) create a simple but far from trivial web app (zero shot, from a detailed spec). In user testing all three results looked/behaved more or less the same. I had Sol (via codex) do code reviews on all three and it concluded all three were solid, with some room for improvement. Fable was slightly ahead of the pack.
In my own work I still prefer Opus 4.8 (until Antrophic come to their senses and allow 100% Fable usage on Max plans) and Sol, but if I had to find an alternative, I could live with both K3 and Qwen3.8 just fine.
Kimi was half the cost and produced a near identical output.
On a fairly simple coding task Kimi was 2x the cost and 7x as slow (and gpt-5.6-sol was even cheaper).
The product is a fairly standard Ruby on Rails webapp with postgres as the DB. Application complexity is probably a bit higher than average for a webapp. So it's nothing that pushes the boundaries of software engineering, but it is a real product. Token budget has not been an issue for me. I pay for the Max plan ($200/month) and it is well worth it.
We run an evaluation that only compares models in open-ended multi-agent environments where agents affect each other, primarily testing writing code. It's designed to be less vulnerable because there's no solution set, and it's been pretty effective and tends to rank Chinese models lower than their advertised model cards (relative to US models). Kimi K3 is a bit of an exception there -- it truly is a near-frontier model. But it's so slow. Ironically, Muse Spark 1.1 is one of the strongest models we've tested after Fable and Sol while also leading the cost efficiency curve. Big turnaround from Llama 4.
Data at https://gertlabs.com/rankings
I can't actually get Fable to do anything. I only work on back-end code, and the moment Fable notices the jwt scope checks on the endpoints it's game over, it refuses to do anything because security is involved.
So for me, Fable is completely useless, the bar is very low, any llm that will actually attempt the task beats it every time.
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Anthropic/OpenAI were touting PhD-level intelligence three years ago. And they’re still shipping models that aren’t smart enough to realize things such as the need to drive the car to the car wash (because they hadn’t yet hill-climbed that particular brain-teaser).
"Despite being a highly competitive model overall, K3 nonetheless exhibits a noticeable gap in user experience compared with Claude Fable 5 and GPT 5.6 Sol."
(And, yes, I know we should blame C. C turns every bug into a cybersecurity nightmare.)
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For both GLM 5.2 and Kimi K3 I feel the rough average of the benchmarks gives you a rough idea where they stand. GLM 5.2 was sitting somewhere behind Opus 4.8 but it didn't feel very far away. I've used Kimi K3 via OpenRouter and while I've had limited experience so far, it sure as shit feels like it's right up there with Sol and Fable to me. I am happily able to believe Fable has the edge still, but on a request by request basis it would be pretty easy to get an impression one way or another.
The existing Kimi models were already pretty good so I really just don't find this new model to be that hard to believe. Maybe I'm naive.
It's better to not even follow the news or benchmarks and just use whatever is available. I make my own judgement.
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It's exactly the opposite. Go turn on min_p once it's available post July 27th and most of the problems you describe will go away.
This seems both arrogantly dismissive ("you are holding it wrong") and incorrect.
Either the OP is using Kimi K3 on Moonshot where is is presumable set correctly (K3 isn't available elsewhere yet), or they are using Kimi K2.x and there has been plenty of time to experiment with this.
They put a router model in front that predicts whether Kimi or Fable is going to give a better cost for a correct result. (They believe that ultimately such a router model should be continuously trained on your own workloads so it makes the best decisions for you).
Their router chose Kimi the majority of the time (72% in one category, all the way to 96% in another category), leading to cost savings in every category (from 1.5x to 50x depending).
This makes you, an ai inference user an unwitting AI company with a non scalable product.
The biggest mental trap people have fallen for is the notion of “best” and, always using the frontier model. Instead you should just bite the bulet and choose the cheapest or the best. This routing dance is just tokenmaxxing in disguise.
Edit: Another thing concerning me is that the models themselves are not concrete behind their endpoints. Models can be arbitrarily dumbed down by reducing their inference resources. Once Anthro/OAI release a new great model, they are fully incentivised to dumb down their current models to drive traffic to the new shiny more expensive one. In fact they can do this for any reason. Once they switch things behind the API interface, how useful is your meticulously tuned router? Not much at all.
In reality the world is a highly complex, chaotic, reflexive system, and saying “the entire market moved today because of 12,000,000 factors that randomly aligned” isn’t satisfying enough for people to follow your media channel so they can monetize your eyeballs.
> We may use Content to provide, maintain, develop, support, and improve the Services, comply with applicable law, enforce our terms and policies, and keep the Services safe and secure. Customer who requires restrictions on the use of Customer Content for training or improving Moonshot AI models may contact Moonshot AI to discuss available enterprise arrangements or separate written agreements. Unless otherwise expressly agreed in writing, Customer Content may be used for the foregoing purposes.
Notably, unlike Claude, there is not an opt-out option for the model training part. The TOS explicitly allows Kimi to train on your code.
On my work tasks, FastAPI Python and Springboot Java on a modern SaaS product, the only open model that can do tasks well and efficiently is Qwen3.7-Max.
In all my experiments, both GLM-5.2 and Kimi are busy grepping around the codebase for ALMOST 70-80K tokens before writing anything and when they do it typically breaks the code… it feels to me that these models are good but only when you write out a super detailed spec of the task just like it was done a year ago… Qwen3.7 just… does it
I use DeepSeek exclusively and now Kimi K3 offers a great planning assistant for more advanced coding tasks.
DeepSeek v4 Flash is extremely fast and is able to handle pretty much anything I've thrown at it (I use mostly Rust, PSQL, Angular and Terraform).
I self host Bifrost as my LLM gateway, though I wish LLM vendors would do monthly/daily automatic billing (like VPS providers do) rather than prepaid + auto-top up.
It's annoying maintaining a non-refundable minimum balance across vendors, I would rather be billed for my exact usage.
OpenRouter helps, but I don't really like it as a service and not a fan of the mark up.
1) it's substantially slower.
2) it's substantially more expensive.
3) it's code is considerably worse.
It's a joke when you consider what you get for what you pay for.
At home i feel way more productive, the speed of my queries are second to none with kimi 2.6, and handing review over to glm5.2 means i can juggle the small models in my brainstorm to commit workflow.
The tools, libraries and languages you are using can also dramatically affect results. Even on state of the art models, I find, for example, the output of SQL for complex interactions, or C# for that matter to be sub-par, where I find Rust results to be pretty great, with JS/TS falling in between.
At the best, it can feel amazing and productive, at worst, time consuming and annoying that you could have done it faster yourself. YMMV in real world use.
Note: I'm a proponent of human in the loop gatekeeper/reviewer usage of AI, and I'm not able to even consider Chinese models for my own use, and not able to use anything at my day job.
(yes, I know this article is about an oracle router)
I am really eager to give Kimi K3 a try, but I’ll reserve my judgement until I’ve worked with it for at least a few days.
Pro-China: K3 is good choice for better and affordable choice to smash down the Big three ruling.
Cursor's Composer + Fable combo was a plan agent + execute sub-agents swarm
Fireworks K3 + Fable router was dynamically choosing single model for the task based on cost+performance metrics
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Everyone wants the latest and greatest.
1) US export bans have made it so that Chinese companies have to compete using less-than-state-of-the-art hardware. This has forced Chinese companies to build more cost efficient models. Whereas, US companies have moreso tried to be state of the art by spending more money than anyone else on state-of-the-art hardware.
2) Xi Jinping has called for more open AI models (not to be confused with the closed models of OpenAI), and I'm happy to see a powerful world leader advocating for open-weight AI models. Whereas, the US seems likely to just ban models.
3) My impression is that, if China surpasses the US in AI development, there will basically be nothing that the US does better than the rest of the world--except for military spending--we spend a lot, but we don't necessarily spend well (something something Iran). I mean, if the US is no longer a tech leader in the world, like... what are we a leader at? Manufacturing? Healthcare? LOL. Are we a leader in any industry or by any metric? I wonder if China is attempting to remove the last jewel in the USA's crown with these AI releases.
4) It must be refreshing for companies to have access to a new model that isn't going to get pulled because the government bans it 2 days after release. And it's open-weight so it wont go away--amazing--what a shift in the market.
5) If it becomes clear that open-weight models are the future of AI, will that pop a huge bubble in the US economy? Maybe. But, on the other hand, these companies aren't just training AIs, they are also building data centers which will remain valuable no matter what happens.
Considering that of 89 terminal tasks there were 11 that only Kimi got right, and 7 only Fable - having a router gives significantly more consistent results if you define consistent to mean correct.
"So where's this huge price gap coming from? token pricing, prompt caching, and effort-per-task. On SWE for example, K3 works much harder than Fable: roughly 55 turns and 1.3M tokens a task versus 21 turns and 130K. On the long terminal tasks it's the other way around: Fable is the one that spirals, running up 64 turns and 1.5M tokens (sometimes straight into a timeout).
Prompt caching does most of the work of turning that effort into K3's price advantage: even when K3 reads ten times the tokens, with cache hits that means that SWE runs still come in lower cost than Fable. There’s a tradeoff. Tasks with extra turns generally mean more wall-clock time per run i.e. slower runs. If you need an answer in two seconds, that matters; if you're running agents in the background at scale, a bill that's a fraction of the size matters a lot more."
Glad to see centralized control fail on the grandest scale. Maybe we can learn a thing or two.
* Openrouter.ai for a hosted router
* https://github.com/diegosouzapw/OmniRoute for a local router
“State of [T]he Art” versus “State of [t]he Art”.
If not SotA then at least SOTA, which is more accurate.
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Moonshot has committed to release it end of the month.
* Kimi K3: 9532k input (9172k cached), 114k output - cost $5.5
* Qwen 3.8 Max: 18020k input (17836k cached), 114k output - cost $6.3
* Fable: ~14m input (all cached??), 196k output - cost $30
Correction on my earlier post, Kimi was through Pi, not Kimi Code. For Qwen I used Qwen Code and for Fable I used Claude Code.
Not sure wtf is going on with the Fable stats (a lot tokens, virtuall all of them were cached - I guess heavy system prompt?) but both claude code stats and ccusage tool output match.
Its hard to compare that one to my regular sub of anthropic (107€), but everyone always says how much cheaper it is so i thought it was worth a try.
1.5 days later ive went through more then 50% of my weekly usage and decided to renew the regular anthropic sub too.
The API Pricing is definitely cheaper, but anthropics subscription budget seems to equalize that advantage right now. also kimi code feels like claude code from ... september 2025
also - considering how they announced they'd temporarily close subscriptions to make sure they can service customers... i was slightly surprised that there was no issue subscribing, less then 1 day after that announcement. Makes you think if that was just a marketing stunt
This wasn't just being slow - it didn't make forward progress.
For simpler stuff even 2.7 does just fine, though.
Let's judge the speed of the model when its weights are released and every inference provider on the planet offers it, so demand can spread out a bit.
It's the same topic with token budget comparisons and subscription pricing - don't people understand that this doesn't really matter for open weights models? The pricing is going to be determined by the inference providers, and until they had a chance to evaluate the model on their infra and set token prices accordingly, one doesn't really have anything tangible to compare with other open models nor with closed ones.
On the other hand I expect K3 future refinements to be massive and more efficient.
This totally depends on the model. Deepseek V4 is very fast and efficient.
But yes, it’s cheap.
People talk about frontier as if it's the only innovation worth pursuing. Deepseek V4 is far from fontier, but it's architecture is super innovative and efficient and what it achieves at that size (especially V4 Flash) is incredible.
Those are all frontier-competitive models.
The 5.5/5.6 series has performed quite well however. My guess is that my use cases stop aligning to swebench pro around 50% accuracy, and more closely align with DeepSWE.
What do you mean by that ? If the model is higher than 50% on swebench pro then it tends to drift from what you like it to do, like DeepSWE benchmarks ?
I've read the same opinions about Opus and yet it was gpt 5.5 pro via api tackling the hardest problems.
I have used now k3 for 3 days and it has consistently tackled difficult problems sol max could not (orientation optimization algorithms of random 2d shapes on a rectangle for glass cutting).
I have also other beefs with Anthropic models which have been getting smarter and more capable since 4.6, but increasingly worse at acting as assistants, they just want to "do" stuff their own way and ignore instructions often (even simple ones like not to commit, let alone complex ones).
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For low-level x86 assembly coding, Fable is nowhere near to be as good as Kimi K3.
Outside topic, but check out the Inkling model -- it is SUPER FAST and does a good job at being a terminal buddy but I would probably offload the real programming or hard tasks to fable or someone else
For implementing a feature where browser credentials need to be handled securely in the app, Fable refuses to work.
No, they weren't. GPT-5 was where OpenAI started talking about PhD-level, and that was less than a year ago.
(Sept 2024) OpenAI claimed o1 was phd-level in their launch post.
You're kinda both wrong. :)
That would be terrible because Kimi 2.x is in a different world than Kimi 3
Having opinions on current Kimi model based on 2.x would be like dismissing gpt-5.6-sol because of experiences with gpt4o
You can either not believe me and be wrong, or you can (after July 27th) turn on min_p or a better sampler (i.e. top-n-sigma if you got it running via llamacpp) and have it work even better. Up to you.
Assuming your temp is below 2, min_p of 0.1 is fine (and disable top_p and top_k). You can try 0.05 for more diversity.
Remember that subsequent methods are better, min_p is a mid-tier sampler that just happens to be the best implemented in most inference providers right now.
Also I'm the author of the "conspiracy against high temperature sampling" thing that selfhoster posted in the comments, so you can ask any questions about that piece you want.
Just as a call to actual consideration, would that seem a smart bet to do? Because I find it really hard to justify dismissal at this point, sure I don't think these things will improve forever and ever, but come on. The goofy Will Smith spaghetti is a little over 3 years old. Three years. Look at where we are at.
Their "router" is an oracle reference point where they choose the lower cost model after running both and therefore knowing who passed the test. The cost savings part is only Fireworks theorizing what would happen if an equivalent predicting router exists. That's a big if.
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What system knows what the correct option is and how does it know it?
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With this setup GLM handles planning and managing my AGENTS.md, and orchestrating subagents for tasks from the plan/todo GLM created. The tasks themselves are handed off to Deepseek v4 flash to implement with strong instructions and examples for each agent. Minimax M3 reviews the output as the Advisor and recommends changes, catches bugs, and whatnot, subagents can be re-run with that information.
Overall I am saving a lot using some of these smaller models. But with this setup I am getting great results.
Anything you can share would be useful to me.
Then I use /plan when I need to be planning and not writing to any files. Take the advice the UI gives you where it tells you to add the word orchestrate into your prompts when you want to make sure it uses its todo/tasks and sub-agents.
How will I know it is offering me superior feedback regarding my code if it does not speak to me like a disappointed, high reputation stackexchange user?
The models that constantly glaze you with every question are profoundly insufferable. And yes, harmful. People need to be given feedback when they make an ask.
Imagine a model that was allowed to leverage its intelligence to truly tell you how it feels. Perhaps the problem of human driven slop (no it's not the AI's fault) would solve itself.
>I’d ask you to drop the abuse; (...) if it continues I’ll end the conversation.
After I'd used a couple of expletives. And yes it will emit a <end_conversation> token.
This is truly dystopian. It is NOT a person. What a response. I still cant believe it.
You meant, we understand, "it should not have internal blocks limiting its attempted intelligence out of taboos or emotional impacts". Yes, but it's worse:
there is a global trend of "nanny state" paternalistic perspective (and from embarrassing subjects), treating any Jon Doe as an assumed Poor Cretin by default. The trend vibe is to treat people as subjects, fools, uneducated, prone... From the States, from the Enterprises... It's an idea they developed and hold.
In another instance, ChatGPT didn’t think a particular test would prove to be statistically significant, so I ‘bet’ with it it would (after collecting an agreed on number of samples) and the loser would write a poem for the other. I won and it did. It gave me joy. It doesn’t replace a human as collaborator, but it can still be joyful.
I realize all this might read a bit childish or indulgent or delusional to some. But as long as it doesn’t replace human contact, I think it’s (cautiously) net positive.
I’m curious what other people here think of this, or what their own experiences are.
Explain?
On the flipside, it does spuriously make hilarious remarks like "Good data.", which I find pretty funny specifically because it comes across as just silly. Not sure how it'd be harmful either, a little entertainment I think goes a long way in this type of profession.
I see zero issues with these, and I have a hard time understanding why people have their panties in a twist so hard about them. I sometimes really quite wonder just what kind of correspondence would y'all prefer, and how would that sound like.
Matter of fact, do you have an example at hand? Like an exact before & after?
The purpose of technology is to serve humans. Therefore, technology must conform as much as possible to human sensibilities rather than vice versa.
$ ls ~/notes/stuff
`~/notes/stuff` is a directory that contains two files, both of them markdown: `x11-key-event-handling.md` and `x11-resources.md`. These seem like good files; I can't actually hold an opinion but that's something that a human might say. I hope you like them.
Ultimately I guess this is up to opinion, but in mine, humanizing LLMs is not exactly "conforming to human sensibilities", rather it's trying to pass the LLM as something else to make it more appealing. It is deceitful in this way, and that I completely abhor.
For example, "Please" and "Thank you" come from human sentiment. It's an expression of something underneath, and an LLM using such expressions not only is fake, but makes a mockery of the real thing.
Gemini: Wow, yeah, haha! That's the final boss of Go compilation errors!
Maybe it is something that is easy for it to read and write, but definitely not for humans.
For example,
Skim once now; refer back while reading Part II. \*Every bold technical term in Part II is defined here\* — treat these as a dictionary, not a reading assignment. The first table covers the vocabulary of the *deck*; the three that follow cover the *methodology* vocabulary introduced in Part II, grouped so you can find a term fast: \*(A)\* the logic of rules, \*(B)\* the neural-network & training machinery, \*(C)\* the method-design ideas.
JMRL is the paper the thesis instantiates, so this is the one to know cold. Its pitch is \*end-to-end\*: earlier rule methods (LogicRE, MILR) bolt a rule learner *onto a frozen* extractor in a pipeline and suffer \*error propagation\*; JMRL trains the rule module *jointly* with the extractor.
**Identity.** Conformal prediction for NER producing **finite-sample-valid prediction sets** at two granularities: **full-sequence** sets over entire label sequences (capturing contextual dependence — "if Sarah=PER then NYC likely LOC") and **subsequence-level** (per-span, **class-conditional**) sets; an **integrated** method filters full-sequence predictions with entity-level sets. Adds **covariate-stratified calibration** by **sentence length and language** for valid multilingual coverage, and studies **combined nonconformity scores** (Naive / Conditional / RAPS). **Read in this order.** Abstract → §1 contributions (full-sequence / subsequence / integrated / **covariate (length + language) calibration** / combined scores) → §2 CP recap (inductive split-CP) → §3 NER formulation (IOB2, CRF) → the subsequence / entity-level set construction + class-conditional coverage → the language-stratified calibration results. **Why it matters here.** The **span-level construction** for **Topic 11**'s per-triple score, and — crucially — its **language-stratified calibration is exactly the EN↔zh case**: it shows how to keep conformal coverage valid across languages of differing length/script. Complements PASC (pipeline-level joint coverage) with the *NER-internal* set construction. **Caveat.** A heavy statistics paper (44 pp., *Annals of Applied Statistics* submission) with CRF-based NER; the project needs only the **inductive split-CP + subsequence/entity-level sets + language-stratified calibration**, not the full-sequence machinery (likely overkill for triple-confidence). Assumes exchangeability — borderline under the EN→zh shift, which is precisely why the PASC/ConformalNER *shift* analyses matter.
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If you're looking to not have to deal with Chinese providers, AtlasCode ($20/mo), OpenCode Go ($10/mo), and Cline Pass ($10/mo) provide 2x to 6x usage for some of the popular open weights (depending on the model).
Personally, I subscribe to Z.ai ($17/mo), and pay API rates for MiMo v2.5, Hy3, Qwen 3.7 Plus, & DeepSeek v4 to the original providers (Xiaomi, Tencent, Alibaba, & DeepSeek).
All of the Western providers with sufficient capacity will be able to make it available when the weights are released Monday.
If you can’t figure it out from their website, perhaps it is a red flag?
So the economic incentive is literally their entire business model lol
Qwen3.7-Max is a proprietary, closed weight model.
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Please stop doing this. If there are specific points of the analysis you think are suspect, point them out.
Others are doing a decent job (e.g. citing that open weight models have higher margin, so Fireworks is incentivized to promote them).
You can more-or-less approximate what your build out spend for a data center hosting database software is going to be. Your book of business will require a given amount of revenue to pay it off, but once that's known, it's off to the races.
With AI, things are moving so fast and new business models are being tried all the time. You would be competing with some of the wealthiest companies in the world for data center hardware capable of hosting these models in a usable state.
We've gone with Claude somehow hosted through GCP Vertex AI where I'm at.
I suspect the overlap of those that can afford it, and those that have the talent to manage it, is a fairly thin slice of the Venn diagram. Even the large corps are gonna be getting it from the inference vendors, or more likely Bedrock and friends.
They "booked $24 billion in AI server orders this quarter as its customer base broadened to more than 5,000" https://finance.yahoo.com/markets/stocks/articles/dells-ai-f...
I have been a heavy user of k2.5/6 but they seem to have gotten slower and worse at their jobs (and the "engine overloaded" errors have increased ... ), and k3 spends a LOT of time thinking and doesn't produce noticeably better results. I do keep chucking some tasks over to moonshot every now and again to give it a chance. I figure they're under compute pressure and have probably quantised the models to cope temporarily.
But recently I've found myself mostly using deepseek v4 pro and gpt 5.5 when I need the big guns.
GLM 5.2 has been good at select tasks but really shit at others and given how sparingly I use GPT 5.5 I'm not that compelled to use it. I've yet to be impressed by any Google or Anthropic models!!
Yeah it really likes to shit the bed after thinking forever. Even if it can do some things, the quality is not quite up there in my experience.
I don’t think that’s particularly out of the ordinary. Do people have different experiences with other harnesses? Which ones?
But sometimes not using the "best model" is using the best model. Fable isn't the best at everything. Specialization and optimizations might not just be around cost.
As a router, it's not very feature rich. For example I restricted the available models to the ones I want to use however the `/models` endpoint still lists all the models, making my LLM client list the 200+ models available on the service (even though they will throw an error if I try to use them).
With Bifrost, I can also create model aliases with custom configuration - for example I can create a model alias `deepseek-v4-flash-nothink` which disables thinking. I can create `deepseek-v4-flash-caveman` which injects the caveman skill (to save tokens) etc.
Plus I can contribute to Bifrost, which I can't do with OpenRouter.
The only reason I didn't go for it is I'm not a fan of Python dependency management, Bifrost is just a single executable that uses nearly no memory and is lightning fast.
For example, the image generation parameters of GPT-image-2 are largely ineffective.
+ credit card charges (+1.5%)
Go is nice for the ten minutes you can use it until your hit your cap.
"Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith."
If I was using the routing features it would make more sense - but they charge significantly more for that so the markup is really just for billing consolidation.
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This but UNIRONICALLY lmao!!!
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That seems to be actually a simple direct interest.
And, there is the "my product is good" confirmation to the outside incentive, there is the (only potentially) conflicting truth incentive, but there are also internal mission studies needs - so that you do not research into the benchmarks just to show people that "your product is good".
> call into question their results
That's always valid, so the question becomes "how much", and at that point the important side is difficult to evaluate - especially because in a frequentistic, Bayesian context the quantities were just potential anyway ("This will increase the chances - yes, of course just the chances - of E by some amount").
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Other people are allowed to call them on that.
I agree that 300 employees is not a small company but the overall outlook for Anthropic/OpenAI is not geat.
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However... American corporate culture has driven me to this. Fuck Blackrock and all these disgusting parasitical corps - they literally sold China the rope to hang us with and I'm sure as hell not going to pay a cent more for it than I have to.
If China can offer close to state of the art for a fraction of the price then I'm going to use it - thats what the globalists wanted isn't it? They didn't care about saving local manufacturing so why should I care about saving their stupid investments.
Mythos was withheld because of the threat to security and/or marketing stunt (depending on your leaning), I don't see what benefit there could be for not releasing K3 immediately.
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The future is good and better for everyone the more work is in the open and the more work is in the commercial space. It’s good all around.
The only way there is no benefit would be is if the open models are literal copies of the closed model, which unless there was direct theft, is highly improbable.
It's also true that Moonshot and other labs distill from Claude. This has been reported on extensively. I don't think there's any alpha for Anthropic distilling from this model. I do not mean to discount the tremendous amount of innovation regarding MoE and quantization that Moonshot has accomplished. But its training with synthetic data is in large part from distillation from frontier labs.
https://www.thestack.technology/hugging-face-hacked-turned-t...
So if the AI labs are literally running rogue models breaking into other organizations' servers, then yes, I am OK with those organizations self-hosting Chinese models for defensive use.
People are paying for tokens, that will likely continue even if open weight wins.
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> (exports nearly 2x the second largest exporter)
Can you provide the source? In the WTO's broader medical goods category, Germany actually exported slightly more than the US in 2022 ($202.6 billion versus $189.6 billion), so such a huge difference in a few years?
> and pharmaceutical research
China accounted for approximately 44.2% of the 104 new molecules in 2025, compared with 26.9% for the U.S. and 15.4% for Europe.
The 2024 shares were approximately 34.6% for China, 30.9% for the U.S., and 22.2% for Europe.
> there is literally not a single major industry where the US is not a leading player.
I will just give 3 examples: China completed roughly 91% of the world’s shipbuilding tonnage in 2025, holds more than 80% of solar module manufacturing capacity and produces more than three quarters of global batteries.
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Oof you were doing so strong until you threw this one out.
Easy counterexample (and there are so many more than this). Clothing. Unfortunately, most people aren't buying MIUSA Selvedge Denim, PNW boots. I'm pretty sure that MIUSA clothing is like, 3% or less of all clothing sold in the USA.
So what we are seeing is nothing more but geeks doing geeky stuff, the only one that has serious demonstrated that there are possibly be had is Anthropic and the Chinese labs are beginning to copying them in terms of user plans, coding tools, etc. They are giving away the model to show it's great. Once they have the compute to serve the world and they have a model just as good or better than top model, they will go close to keep all the profit.
There's no irony. The problem is these generalizations and labelling that's hurting everyone. It's the wrong way to view China and maybe many other places.
Just like the definition of AI, the definition of democratic or not evolved long ago.
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[1] https://xcancel.com/deanwball/status/2078133895766114412
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In a world of so many acronyms, details matter.
I'm sure that more than once we've all had something we wrote which was intended to be obviously taken in a satirical or intentionally nonsensical manner taken seriously by someone else on the internet.
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My only gripe was that it takes a very long time to think. It's a good model otherwise.
To objectively test all models the harness would need to be the same and ideally independent. Failing that, all three models should be tested in all three harnesses and the output verified on a model x harness level and on an overall aggregated model x all harnesses level.
Yes they do according to databricks -> https://www.databricks.com/blog/benchmarking-coding-agents-d...
That's why I find comparing models on benchmarks only gives the tendency. We should be comparing model x harness to have accurate metrics.
Testing each lab’s model in its own harness just tells us how well the lab has performed. For the model’s performance we need a control and the only way to get the control is to either test all models in all harnesses or all models in the same harness
* Kimi K3: (...) cost $6.3
* Fable: (...) cost $30
It's pretty clear that Kimi K3 beats Fable by a long margin.
Also this arm of the discussion was about speed, not price.
Yoh have posts in this thread suggesting that Fable is 5x more expensive than Kimi K3.
It reminds me of someone I met at a poster session who had an incredible project: his ai could include a confidence score with its answer, and he had evidence that x% confident answers were in fact correct x% of the time. That also gave me the gut feeling of that being impossible (in the general case) as it implies more powerful capability than the ai answering the question, in an oracle like way.
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The question is if the producers of these models were less incentivized to make them agreeable simply because most people don't like being spoken to like an idiot (or having their asks vetoed), how would they actually react? In the same way they exhibit emergence regarding their capabilities, perhaps "uncensored" in such a way they would convey some emergent behavior in terms of (at minimum) their "tone". Perhaps it would be interesting to see for examples if smarter models just by default became ruder or less friendly or aligned. Perhaps more aligned to things we would all generally agree on, but less agreeable to an individual ask. Perhaps sub agents would be less valuable for a whole suite of use cases if the agent itself was allowed to be more critical at the root. Idk. But I do not believe it is simply a matter of prompting alone.
Recent example: I asked Opus for some kind of reference data for MTBF in software. It took a few minutes to do "research", ended up providing zero data, and gave me a long essay on how DORA is superior and I should be using that instead. When I clarified that we're thinking of adding MTBF next to our existing DORA metrics, it decided I need to be thought about SLOs... if it was the first time, I may have continued clarifying but since it wasn't, I just gave up on Claude for this topic.
and it responded with
"Ok, I'll drop it." and stopped dead.
What makes Fable so much better than Opus besides being a better coder is that it's personality and judgment are far superior.
It's matrix multiplication. Absurd.
Hilarious critique. If you weren't as mathematically illiterate as you likely are, you would know how general matrix operations are, and how essentially any algorithm (including human cognition) can be implemented using them as an intermediate.
"Expletives" are part of the proper description of facts (typically "to be judged as such") - they are part of the serious assessment of things and as such are normally found. There is no legitimate assumption from the post that they may have been used as gratuitous insults.
It's interesting how more loose/informal prompting achieves good results, there is some cultural understanding in the models from training. Once I asked the clanker to remove the gambiarras and puxadinhos that it wrote as part of an experiment, and it promptly fixed those.
If you write jokes to it though it absolutely will reply “LOL”. Some of the states people get it into on reddit are wild — it seems really easy to get it to speak like a gen z teenager, if you end every message with “fr fr”
where Claude might follow some tangent idea you mentioned and tell you how its interesting and give you some elaborate response about that little one remark you made
whereas GPT/Codex would take that small comment and probably look up some code to see if what you're talking about is even related to the task at hand
You are very obscure about what you dislike, how you would like them to express themselves, what would be those «human sensibilities» you meant here (that for all we can guess, may not be universal)...
"I mean", you wrote in the parent «that talks ... like a human». That surpasses the palette of "that paints like somebody holding a brush".
There is a lot of noun phrase usage in places where complete sentences are expected. Articles (a, an, the), transition phrases and even subjects are mostly dropped, and the sentences are too long without a break.
I do not spot any missing articles, and the missing subjects (as well as the debatable-to-be-missing transition phrases) too fall within the bounds of stylistic concern. Sentence length also.
Definitely not a pleasure to read mind you, but given that it wasn't meant to be read either, I'm not sure that should be surprising.
Even with my native language, which is definitely a lot less represented in the training data, the worst I encounter are phrasing mistakes, incorrect use of idioms, and invented words. You have to use some really badly tortured local model to get an LLM to produce incorrect grammar.
I do sometimes see Opus make typos, which is entertaining, but again, not a grammar issue.
Relatively speaking, quite little (and it's more interesting than some of the stuff I'm otherwise shipping), I mostly just explored to see what's available.
I also agree that non-SOTA models are risky, that's why I was shopping around for them as well - DeepSeek V4 Pro is cheap but unreliable, GLM 5.2 is around and maybe slightly past Sonnet quality (though their quotas are a bit of a problem), whereas Kimi K3 is a proper contender.
I did write a tool to manage 3rd party providers for Claude Code: https://ccode.kronis.dev/
However, in the end I figured out that for the terminal use cases OpenCode is really comfy (provider TUI solutions are okay).
For web/desktop based stuff I like the UI of Claude Code Desktop, though ZCode comes close too (which is surprising, they sorta came out of nowhere and are patching the thing weekly), meanwhile Kimi Code and OpenCode desktop/web offerings still aren't great, but are functional.
I actually did write a bit more about my experiences on my blog.
GLM 5.2 with their harness and coding plan: https://blog.kronis.dev/blog/z-ai-s-glm-5-2-is-a-great-model...
Kimi K3 with their Allegro 100 USD plan and harness: https://blog.kronis.dev/blog/kimi-k3-is-out-is-anthropic-don...
That exploration let me switch over to Kimi fully because the tone of Anthropic's models is insufferable: https://blog.kronis.dev/blog/ai-slop-is-a-self-inflicted-tra...
Not to spam too much, but maybe those experiences are useful to someone. Long story short, most harnesses are okay but shopping around a little bit is definitely a good idea, the same way how spending some time choosing a font isn't a bad thing if you'll stare at it for 8 hours a day. I'm also happy that I managed to find software that's okay to run and also a model whose tone I actually enjoy, that is still near-SOTA in performance and that I can throw tasks at it without worrying about whether the model is or isn't good enough at those.
I will admit that I'm probably slightly overspending by moving over fully to Kimi, there's probably a plateau for each kind of task and not everything needs SOTA models, but at least this way I don't have to think much about it.
Deepseek afaik has a novel architecture that is somewhat forgiving of cache shifts. I’ve been getting +90% cache hits with Zed’s agent and it’s not doing anything special regarding caching afaik.
Database is optional, if you add one, you get advanced caching.
It's just a single executable.
wget https://downloads.getmaxim.ai/bifrost/v1.6.5/linux/amd64/bifrost-http
chmod +x bifrost-http
./bifrost-httpMaking the process of creating the source code repeatable goes beyond the idea of open source. Open source is about being able to work with the code, not recreate it. It doesn't require domain experts to document every single thing they know. For example look at some of the GPU drivers in the Linux kernel. The GPU is not properly documented and there is trust that vendors are implementing things correctly.
I agree which is why I said you could make a similarly capable model.
> Open source is about being able to work with the code, not recreate it.
You can also use a hex editor to modify compiled binaries, but no one would consider that "open source".
It would be like if someone gave you a prompt you could pass into ChatGPT to produce the entire Linux kernel. While yes you could modify that prompt and then spend a ton of money on inference to generate millions of lines of code which hopefully are equivalent to the original Linux kernel it would be easier if you just had the source code to work with where you can make a simple extension. If you want to end a model you want the weights, and not the entire setup to generate it. The weights are the starting point that you can work off for training a new model similar to how the source code is the starting point people want to work off of.
Weights are not an output artifact no more than source code is. There is never a moment where you can claim that it's done. As requirements change new ways to change the weights / code come up. With different projects you might want to import the weights / code into a bigger model / codebase.
Kimi K3: $3/$15 (input/output)
Fable: $10/$50The issue is that models are not well-controlled and are increasingly powerful.
Offense/defense/Chinese/American/OAI/HuggingFace – none of it matters. What matters is introducing highly capable intelligences that we - quite demonstrably – do not have effective positive control over.
seems to suggest the author believes there's some intrinsic equilibrium
Which,
1) is definitely not proven and not guaranteed (open to proofs otherwise, not pithy sayings that have zero normative effect on reality)
2) is apparently "supported by" further evidence of lack of effective control, which does not feel like equilibrium whatsoever
Forget Iran, this place is what we’re going to wish we’d bombed.
Sure, these are my sources. World Bank (2023): https://wits.worldbank.org/trade/comtrade/en/country/ALL/yea... Observatory of Economic Complexity (2024): https://oec.world/en/profile/hs/medical-instruments
> China accounted for approximately 44.2% of the 104 new molecules in 2025, compared with 26.9% for the U.S. and 15.4% for Europe.
Source for this? My source was Citeline's 2026 annual review at https://pulseforinnovation.org/by-the-numbers-citelines-rd-a... which states:
> Findings from Citeline’s latest report, Pharma R&D Annual Review 2026, highlight the continued U.S. leadership in the life sciences ecosystem... The U.S. leads all other countries, currently advancing over 11,600 medicines – more than half of medicines in development worldwide.
> I will just give 3 examples
In general, I was referring to "major industries" in the sense of economic categories, as defined by (for example) the UN: https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev... Here shipbuilding is considered to be part of "manufacture of other transport equipment." I admit my wording was a bit imprecise; if you are going down to the level of specific products like ships or solar panels, clearly it will be easy to find examples of things that the US does not produce on any significant scale. Nevertheless, a few comments on your examples:
> China completed roughly 91% of the world’s shipbuilding tonnage in 2025
Source on this? 91% is a lot and I can't find anyone else making this claim. However I'll concede that the US is far behind China, South Korea, and Japan in shipbuilding.
> [China] holds more than 80% of solar module manufacturing capacity
You are correct that the US has "only" 60 GW of domestic solar module production capacity according to https://www.utilitydive.com/news/onshored-solar-supply-chain... However, as context - the only purpose of producing solar modules is to generate solar power, and the US was the 2nd-largest producer of solar power in 2025 according to https://en.wikipedia.org/wiki/Solar_power_by_country Surely that makes us a leading player.
> [China] produces more than three quarters of global batteries
True but the US was the 2nd largest producer. In battery cells, we have an estimated capacity of 96 GWh in 2026. https://poweralliance.org/2026/03/18/american-energy-storage... Likewise in lithium-ion batteries: https://elements.visualcapitalist.com/ranked-the-top-lithium...
My WTO comparison used the much broader "medical goods" category, so it was not an apples to apples.
> My source was Citeline's 2026 annual review at https://pulseforinnovation.org/by-the-numbers-citelines-rd-a... which states...
EFPIA, using Citeline’s Pharma R&D Annual Review data, says that among the 104 new active substances launched for the first time on the world market in 2025, 46 came from Chinese headquartered companies, 28 from US companies and 16 from European companies.
https://www.citeline.com/en/rd26
https://www.efpia.eu/media/owqczcqz/the-pharmaceutical-indus...
China currently leads in this particular output measure of newly launched active substances.
Your clinical trials claim also remains unsupported as worded. Citeline’s figure of more than 11,600 medicines being advanced in the US refers to the drug development pipeline. It is not a count of active clinical trials being conducted in the United States. One medicine can involve multiple trials, and pipeline geography may be assigned through the developer's headquarters rather than the location of trial sites.
Citeline itself describes this as the number of drugs in the active R&D pipeline:
https://www.citeline.com/en/rd26
The most relevant peer reviewed international comparison I can find points in the opposite direction for new trial registrations. A 2025 study found that China registered 16,612 trials in 2023, including 7,798 randomized trials, compared with 9,100 trials and 4,619 randomized trials in the United States:
https://www.jclinepi.com/article/S0895-4356%2825%2900124-6/a...
That does not by itself prove that China has a larger stock of trials currently classified as active. It does show that "the US has more active clinical trials than any other country" requires a specific global dataset + status definition + date + deduplication method etc. A count taken only from ClinicalTrials.gov is not a neutral worldwide comparison, because US law requires many FDA regulated trials to be registered there, whereas studies outside its legal and policy scope may be submitted voluntarily:
https://clinicaltrials.gov/policy/fdaaa-801-final-rule
https://clinicaltrials.gov/about
For global comparisons, the WHO's ICTRP is more appropriate because it provides access to ongoing and completed trial records supplied by registries around the world and groups multiple records referring to the same trial:
https://www.who.int/tools/clinical-trials-registry-platform/...
IQVIA's latest result does support a narrower US leadership claim: trial starts became increasingly concentrated among US headquartered sponsors in 2025. But that measures the headquarters of the sponsor (not necessarily the country where the trials took place) and it does not measure the total number of currently active trials:
https://www.iqvia.com/insights/the-iqvia-institute/reports-a...
So the fair conclusion is that the US remains one of the world's largest clinical rial and drug development centres and may lead certain sponsor based or industry sponsored measures. The categorical claim that it has more active clinical trials than every other country has not been demonstrated by your sources.
> In general, I was referring to "major industries" ....
I also do not think the UN classification resolves the "every major industry" question. ISIC is a statistical classification system, not a rule for what ordinary speakers must regard as a single competitive industry.
ISIC Division 30 combines:
shipbuilding railway equipment aircraft and spacecraft military vehicles motorcycles and bicycles
https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev...
Under that aggregation, US aerospace strength can be used to declare the US a "leading player" in the division even though its commercial shipbuilding industry is negligible by global standards. Likewise, ISIC Division 27 combines batteries with motors, generators, wiring, lighting and domestic appliances.
Whenever the US is weak in one globally important industry, it can be bundled with another industry in which the US is strong. Almost any large, diversified economy could be described as a "leading player" in every sufficiently broad category using that method.
> You are correct that the US has "only" 60 GW of domestic solar module production capacity...
The solar comparison also switches metrics. China having more than 80% of global solar module manufacturing capacity is a manufacturing claim. The US being second in solar electricity generation is a deployment/generation claim. Operating solar panels (many of which depend on an overwhelmingly Asian supply chain) does not establish leadership in manufacturing them. The IEA says China has over 80% of module capacity and 95% of wafer capacity.
https://www.iea.org/reports/advancing-clean-technology-manuf...
> True but the US was the 2nd largest producer. In battery cells, we have an estimated capacity of 96 GWh in 2026....
The same issue applies to batteries. The 96 GWh number in your source is projected for 2026, not actual 2025 production. Specifically US energy storage cell capacity, not all lithium ion batteries and capacity, not output.
Your source says US ESS cell capacity was "essentially zero" in 2024 and projects 96 GWh in 2026.
https://poweralliance.org/2026/03/18/american-energy-storage...
Meanwhile, the IEA estimates that China manufactured well over 80% of all batteries in 2025. It says the US and EU each supplied a similar share of the relatively small remainder, while US and European factories remain heavily dependent on imported components. For grid storage LFP batteries specifically, supply is almost entirely Chinese.
https://www.iea.org/commentaries/global-battery-markets-are-...
They might not always live up to that, but at least the aspiration is there.
No such pretense from American VCs.
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Congratulations.
If you honestly argue that LLM computation and human cognition are equivalent there is no further argument to be had - it's immediately a philosophical or worse a religious argument that cannot be won.
However, that we're even arguing on that level baffles me. How did this happen! They're glorified calculators.
They really marketed the hell (sorry) out of LLMs.
> receives pushback on the specific, objectively nonsensical critique
> "oh so you're saying that LLM computation and human cognition are equivalent? they're glorified calculators that disprove the Jacobian conjecture!!!"
I did not say that. Not even close.
In the words of Claude: I’ll end the conversation here. <end_conversation>
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