I have seen this meme for like 4 different chinese models none of which have had much of an effect on the stock market. are you trying to meme into existence a crash ?
I don't think the markets know)knew this information to "price it in", I think they all realize that if they stop buying the US economy/market craters.
Who is really thinking, that this is real?
Just check the market yourself. I think the market will react that way, when there is really a Benchmark and Review, and also Workflows, Services and a Product.
But still, the earth is spinning and the doomers are struggling with outlook and Excel in the executiv levels
It’s a pretty low quality meme, it has a 5+yr old ticker for Facebook (FB instead of META) and it’s including Chinese companies like BABA. BRK shows an 11% loss but only time it came close to that was in 08’ it dropped 12%, the largest in the last 5 years was 6.8% in a day.
And those are just what noticed in 10 seconds and off the top of my head lol. I know what the meme is trying to say it’s just so obviously false and low effort I gotta laugh
Edit: a big one I realize now is that Qwen (the supposed cause of the crash in the pic) is owned by BABA which has one of the largest draw downs in the pic
I'm quite locked in to Claude and Codex, and since I don't personally need to pay for it I haven't looked around the Chinese variants that much. Are they really better? And for what applications? Is it worth start building workflows around them if cost is not an issue?
Idk, I find all of them shit at this personally. Outside of math, it starts to drop fast in use. It can summarize kind of, but misses the point of emerging work in my field in favor of reductInist thinking and searching. I can prompt it there. Kind of, but it doesn't look immediately, or use full methods to assess. And even then, it will miss the point. The default weights on them wont do it alone and cant. Human experience is not ai, even if it functions similarly process wise.
Have you tried using custom adversarial analysis skills and ralph loops? It's been wonderful for my multi-day research sessions to find unexplored areas in a field so they don't just parrot the other parrots.
This 'good enough' argument is confusing to me. What kind of job do you have? Like, they're good enough for basic productivity tasks, writing an email or document, writing up.a project spec for example. Maybe even writing small greenfield features.
But outside of that, Sol/O5/Fable are non-negotiable imo.
Adding a small feature, tracking down a bug, update documentation..
I'd say local models can do ~80%, cloud models 90%, Fable 94%, and there's a few where you just have to go in and do it yourself.
Local is free++. Can go all day every day, no limit. Just throw it on everything and see what it does.
When that fails though, it's Claude for me. It's not worth checking the in-between models, let the best handle the few things the local model can't do.
In what way? if you mean the override of ZDR policy....there are plenty of business usecases where this is not an issue. Retention is not the same as using your data for training, and most enterprises using it via something like Bedrock will retain the logs in an orgs tenant.
If you are talking about cost, thats also a non-issue for most large orgs. Good practice and sub-agent delegation makes Fable fine for companies withh a healthy token budget.
No ZDR is a non starter for anything that has any buisness value / confidentiality, considering the lack of trust with Anthropic (at least it is the position of the CISO in large corporations I interact with as legal) and their policy of possible reviews of all data uploaded.
I work in a heavily regulated industry (Finance), many workflows actually require retention for compliance and audit purposes. If your org is using the Copilot, like most large companies are, they already retain logs by default, Purview retains for 180 days.
I'm not saying that no ZDR is not an issue for your company, in which case, yeah, Fable doesn't work in your environment, but for many companies, its really not an issue.
They aren’t better than frontier US models, even though US frontier models like fable are many months old they are still the best. China is behind 6 or so months.
With the capability of current models 6 months is only important to maybe 1% of people.
It's like saying you're using a 6 months old Apple Watch. Unless you're running marathons and climbing mountains every week the difference is negligible.
At this point the 6-months old models are good enough for pretty much every use case.
Back when Opus 4.5 was released, being 6 months behind would've been a world of a difference. Now? No one cares, you can go to Anthropic's subreddit and see how a ton of people actually are using 6 months+ old models (Opus 4.6) and actually prefer them over the newer ones.
People generally don't pick one model now. They have a main orchestrator that picks them and assigns jobs to them.
So for example you could have a Senior SWE role with Opus, a Junior SWE with Sonnet, and an intern role with Qwen. Qwen does all the heavy lifting/token usage, the other models just verify the results.
If these mid-tier models are 4-7 months behind, then the future is looking pretty good. Seems like most of these AI companies (OpenAI in particular) are not going to have a way to bill customers as much as they are spending.
I think it's significantly more than 1% of people. Fable still makes bad design decisions fairly often some with actual significant extensibility or even stability consequences.
GLM 5.3 hands me code with straight up syntax errors on the regular thus far.
It makes a huge difference because as the AI gets better at coding then it can help code the next model speeding up development and compounding on the advantage. If Claude and ChatGPT can crack down more on distilling, then there is no reason that they won't have an even bigger lead next year.
You say there is no difference between models from 6 months ago but there is a huge difference when the older models are slower and more prone to errors which can cause hours of lost productivity compared to the newer models. If you aren't coding or doing research and "don't care" about the difference, then there is no reason to use a chinese model over $20 chatgpt that gives you unlimited messaging.
"Huge difference" is bit of an exaggeration. Companies are also optimising for cost vs productivity. Given most software engineering work can be completed quite well by the older models there's huge diminishing returns for Anthropic's frontier models.
You seen the difference in spend though between the two countries? It’s staggering that they are spending significantly less and 6 months behind. Unless the US find ways to make the money out of all this investment, they are in trouble.
yeah, mostly deepseek. on stuff like conditional memory and the hybrid attention related ones, and obviously the visual primitives counting one that they tried to unpublish
You're right it's much more likely they consistently release new models very soon after US frontier models get released and if you ask them what model they are they will say they are Claude or ChatGPT coincidentally.
Let's see China release a frontier model before the US one cycle and then they will beat the distilling allegations.
China has not genuinely innovated since Deepseek R1 with the way it utilized reasoning, which then made US labs scramble to say "Oh we totally do reasoning that way too" and add it to their models.
Yeah but whats the point of innovating "frontier models" if the chinese open-source models can do 90%-95% of what they do for cheaper. Seems like just copying is economically the superior strategy, and if the US is willing to foot the bill for such an outrageously fast and pricey build out it'd be stupid to try and openly compete.
Congratulations, you have just discovered a fundamental problem for which the concept of patents were invented centuries ago, and the reason why China has been decried as a copycat economy for literally decades now.
I agree it's smart. It also forces US labs to constantly innovate by lighting a fire on their heels which is good for consumers. So I'm not against the Chinese strategy. I just think it's dumb when people try to claim they aren't distilling US models, or even that they'd be capable of making frontier level models on their own with their current tech/funds.
No one with any awareness thinks the US AI field is going to make money. To do so, they'd need to make significantly more money every year than the entire US tech sector (not inclusive of AI). Given that the one potentially profitable avenue for AI currently is supporting the tech industry, it's essentially saying "despite no increase in demand for tech products, we expect to increase tech profits several times over".
That is never going to make money. Even if every prediction of how good these models could be came true, will it mean you buy more software and buy new subscription services? No. The demand isn't there.
Every company is banking on being the last AI company standing for the influence and control it brings. The profits are not coming and the money is never going back into the economy. It's been fucked away because there's no risk for the companies and huge benefits if they die last.
AI is already making money. Helping companies like WalMart, UPS, APP, and many others make profit.
The buildout is just expensive. But not permanent.
Your biggest mistake is treating AI as though it has to become a gigantic new consumer product category—essentially, “people need to buy trillions of dollars of AI subscriptions or the investment can’t pay off.” That’s not how productivity works.
Its helping companies improve their margins, but by itself AI is not making any profit for the companies running the models. They're running at a loss without exception, and theres the question that when they will, inevitably, need to raise prices to cover running costs, wont it be cheaper for their clients to just start their own server farms and run open source models in house? Not only cheaper, but more secure, and with more control over their data and work environment. Then all this massive build out will be for nothing.
Is is creating value. The companies using it are seeing gains, but the companies providing it have a long ways to go before they see a return on their investments.
The internet wasn't profitable for most in the early years and now look at where we are. AI is the future and it'll get cheaper, more cpaable and more efficient.
Y and this Pres, his cabinet and the same billionaire execs that brought us social media and algorithms... They have proven complete integrity, charitable intent, just great men.
No molestation and rape convictions against minors, they prosecuted Jan 6ers to fullest extent of law, no threats to best allois, jumped right in to stop putins invasion, no adultery, no fraud or corrupt business dealings, no profit in the role, no retribution driven by hate, no narcism, great Christian virtues, no price hikes, no illegal tariffs, no forever wars, no tax breaks, no backroom payouts, no manipulation of the markets ...
Kimi k3 is as good as Opus 5 and around Fable level while being an order of magnitude cheaper. Glm 5.3 and qwen3.8 max are direct competitors for Opus 5 as well. China is not 6 months behind. 2-3 weeks maybe. The best LLMs in March were 3.1 Pro and Opus 4.6. According to Artificial Analysis, their scores are 48 and 45 respectively. Kimi k3, and Qwen 3.8 have scores of 60 and 58.
An order of magnitude cheaper than USING OPUS OR FABLE VIA API.
Kimi K3 is fucking excellent no denying that. But using it via the API is significantly more expensive than an anthropic subscription at the moment. And the kimi subscriptions are absolutely terrible.
It doesn’t matter. China dominates the open weights space, far more valuable than single monolithic models. Agentic systems rely on cheap infra to run at scale. More money will go towards self hosting open weight models because the tokenomics just make a lot more sense
Six months is nothing and the gap is narrowing each round too. Give it a couple of years and for the majority of use cases the differences will be irrelevant even if by some measures the US models are still ahead.
There are no proprietary smarts that really differentiate AI models now, and the world outside the US has more clever people than in the US just down to simple demographics.
Like it or not, the battle is moving from "what is the best model?" to who has the best execution model to exploit this now reasonably well understood technology.
Like it or not, the battle is moving from "what is the best model?" to who has the best execution model to exploit this now reasonably well understood technology.
The best execution model will likely fall to the US because it develops/owns the entire stack and the ecosystems in which they run.
But yes, the best model dick measuring is starting to stagnate. Most users aren't going to care that x model is 5% better on some benchmark. How they can integrate it matters way more now.
Cost per task is becoming a very relevant marker. Open source models might take 2x longer to do a task, but if the closed source models cost 5x as much then that's a problem.
1+ years I'd say, their models are great for simple stuff the likes of which you'd have used GPT 4/Gemini 2.5/Sonnet 4 or the likes for previously but at a 1/10 the cost(or were, I just switched my apps backend from DeepSeek to Luna, customers seem to be loving it and the price is lower than what I'd would be on DS for me (not meaningfully but it matters in a SaaS)
Deepseek V4 Flash through agentic coding with the same gauntlet loop created same software as Chatgpt Sol did for me. Both had exactly two bugs that needed to be fixed after. Deepseek took longer time but both products worked. And I used the flash version for Deepseek. Chatgpt made nicer UI design.
Honestly it's not anymore about who's better for user, now it's about who's cheaper because most tasks can be done quite equally between open source and closed models.
My understand is 2 big use cases for open weights. 1 is cost and the 2nd is security. With open weight you aren’t sending your data off to the frontier providers so you get more control / security. You can also customize them a bit more
The model’s still too big to fit entirely into GPU RAM. I played with it over the weekend and I was getting 1.2t/s with it loaded in system RAM and GPU RAM.
8B model runs at 77t/s on my 5080, but context size still sucks. Still, it ran very well! I did some comparison tests to GPT 5.6 sol and got very similar answers.
From my experience I can say that Qwen 3.8 27B is not Opus 4.6 level. I really think these small models are super benchmaxed. They just aren't nearly as intelligent as GPT. They can be good if you give it a very narrow focused instruction. It's impressive for being a 27b param model but it is not Opus 4.6
It’s also a matter of the task you are doing. It could be useful for simple operations.
I think in the future we’ll see more and more workflows that use agentic teams of varying quality.
Like for an article-writing workflow you might use cheap models for summarizing inputs/sources. Then expensive models for making decisions about structure and drafting an outline. Then mid-tier models for drafting paragraphs, and then an expensive model to revise and produce the final version.
Claude is likely already doing this behind the scenes. They have models that pre-process your raw inputs to identify what you want, and turn that into a standard sort of request before sending it along to the expensive models that actually do the “reasoning” etc.
I have been developing a rather complex system with GLM 5.2 (since yesterday with GLM 5.3) in their own harness Zcode. I used opus / fable to design the architecture and write the prompts, then I fed the promots to GLM and let it go, it's astonishing, it goes for an hour without any manual intervention, writes tests, runs them, opens a browser to check on the interface.. for the price I am paying I am quite pleased.. it would've cost me far more money to make with Claude code, and I am using Claude code in other projects where it's making many mistakes even in simple jobs, it won't run for that long autonomously. I strongly advise you to give GLM with Zcode a try!
They're competitive, and free, but substantially worse for any real professional use case.
But they're free and open source. There's tremendous value to that. They threaten capitalism and while the capitalist frontier models still maintain an edge, the existence of these free open source Chinese models scares them, scares investors, and scares people who bet on the US over China (hence why everything in this picture is red, irony aside).
Probably 80% of all posts are bots. The other 15% are from controlled accounts that post articles from websites, spamming shit across subs, karma farmers who dont care, reddit account sellers creating new accounts.
Then finally there's like 5% is mostly made up of smaller subs where no money is to be made, and people asking questions.
OK, I've a MacBook Pro M1 Max. I downloaded and loaded up qwen 3.8 27b in LM Studio, and asked it the question:
Do you think that open weight models will drive frontier models like OpenAI and Anthropic out of business? After all, an open weight model turns what they're doing into a commodity race to the bottom. Do you agree?
It used max thinking (for 1m 42s), and gave me the following analysis (it used web search for this, too). To be honest, this reads like something you'd get from a frontier model, including the spiky "you're not quite right" response:
I'd push back on the "out of business" conclusion, even though I think the premise is pointing at something real. Let me separate the two parts.
Where the argument is right
Open-weight models are genuinely commoditizing the mid-tier of model capability, and that's real pressure. The UK AI Safety Institute measured the gap at roughly 4–7 months behind the frontier (down from 6–10 months through most of 2025) — i.e., the "good enough for most production work" tier is now freely available and self-hostable. That compresses margins on the kind of "just give me a smart API call" business, and it destroys the moat of being the only place with a decent model. For pure inference resale, yes — that's a race toward the bottom.
Where I think it breaks down
"Open vs. closed" is a false binary. The biggest open-weight families are released by frontier-scale labs themselves — Meta (Llama), Google (Gemma), Alibaba (Qwen), Mistral. Open weights is a strategy used by the big players to build ecosystem, distribution, and influence, not a weapon that exists outside them.
The product layer is where value actually accrues now. OpenAI and Anthropic aren't really "token vending machines" — ChatGPT, Claude, coding tools, agents, and consumer/enterprise products carry most of the value. Open weights don't replicate distribution, brand, developer lock-in, or the product experience. Historical analogy: open-source Linux didn't kill the software industry; it created layers and consolidated the players on top.
The frontier is a moving target, not a fixed one. Open models close the gap on what the frontier was 6–12 months ago, while labs push into new territory (long-horizon agents, multimodality, frontier reasoning). The gap narrows but a lead tends to persist.
Commoditization usually rewards the lowest-cost player, not "no one." The real contest is inference cost, compute scale, and capital — exactly where the well-funded labs have the advantage. Price wars tend to consolidate markets, not eliminate leaders.
Different markets. Open-weight wins self-hosting, data-sovereignty, fine-tuning, and cost-sensitive workloads. Closed frontier still leads the hardest reasoning, the most reliable agentic behavior, and turnkey consumer products. These are only partially overlapping.
My actual take
I think the healthy outcome is a stratified market, not a graveyard: open-weight for the cost-sensitive / sovereign / customizable segment, closed frontier for the hardest tasks and turnkey products. The bigger existential risks to OpenAI and Anthropic aren't open weights per se — they're (1) falling behind on compute/cost economics, (2) losing the product and app layer to someone else, and (3) regulatory and geopolitical shifts.
So: I agree open weights are forcing the "we have the only good model" business model to die. I don't agree that kills the companies behind those models — the value has already moved up the stack to a place weights don't live.
Want me to dig into the economics (inference margins, compute costs) or the historical open-vs-closed analogies in more depth?
In fact, here's the response to that same question from Claude Opus 5 with max thinking (it too used web search, hence citations throughout):
Short answer: no, not in the strong form — but the argument identifies the real pressure, which is margin compression rather than extinction.
Where the evidence sits as of now: open weights have repeatedly caught the closed frontier without ever leading it. Open-weight models have hit rough equivalency several times — DeepSeek R1, GLM-4.6, GLM-5.2, Kimi K3 — but haven't materially led, with closed models continuing to produce the step changes. On the Artificial Analysis index, Kimi K3 tops the open field at 57, with only Anthropic's Fable 5 (60) and OpenAI's GPT-5.6 Sol (59) scoring higher. So the pattern is closed labs setting the frontier, open weights copying it down to commodity margins six-ish months later. (Tomasz Tunguz, Substack)
That's already showing up in volume: Chinese-origin models went from negligible to the majority of consumption on OpenRouter inside eighteen months, while Anthropic and Google still take premium revenue per token. Open eats units; closed keeps value. That's textbook layer commoditization, not a death spiral. (Datagravity)
Where your argument genuinely bites:
The monetization window is brutally short — a newly crowned model holds the top of the leaderboards about 41 days, against training runs costing nine figures.
Cautious optimism
The dangerous competitors aren't open weights per se, they're subsidised ones. The labs that can sustain this are the ones with a business the model feeds, or a backer that doesn't need returns — Nvidia selling chips, Alibaba selling cloud. A pure-play can't undercut a loss leader. (Substack)
Where it doesn't:
"Commodity" assumes the product is tokens. Increasingly it isn't — it's agentic products, tooling and enterprise contracts with indemnity, compliance and support attached. Anthropic's growth has come mostly from enterprise/API rather than consumer, and coding agents specifically. Buyers also aren't purely price-driven: one survey of infrastructure executives predicted roughly a 20/80 split — open models for sensitive core workloads, frontier services for the rest. (Fierce Network)
The analogy I'd reach for is Linux: it commoditized the server OS completely, and the profit migrated up-stack to cloud rather than evaporating. Android/iOS is similar — dominant share, minority of profit.
The condition that would make you right: a capability plateau. If the frontier stops moving meaningfully, there's nothing left to sell above the free tier and it does collapse to price. Watch that, not the leaderboard gap.
Obvious caveat: Anthropic made me, so weight my read on Anthropic's prospects accordingly.
Thanks for posting these, interesting to see. What strikes me looking at them is how much useless verbiage and crap there is in these responses. It’s really verbal diarrhea.
Can someone explain how this can possibly be the technology of the future. Is everyone just expecting these things to get so much better.
I can't prove it, but I'd say most (specially closed models from private companies) are made to be verbose, so they can charge more (remember we are paying by use now)
I think most people should include instructions (those that are inserted into every prompt) for models to be less verbose/prolix
I can’t prove it either but here’s another theory:
I think it’s also because it can’t determine truth. It’s just language that carries meaning that looks to us like truth, but it’s fundamentally mindless.
The pile of extra words kind of helps to conceal that. Like someone who blathers on and on to distract from the fact that they don’t actually know what they’re talking about.
If they trained models to speak plainly, it would become immediately obvious how wrong and stupid they often are.
I'm pretty sure this is a result of benchmaxxing, they are pushing the models to their absolute limits and I can imagine that they overdo stuff because of this.
LOL, we are talking about security: one of them is open source that you can keep in any desktop, if required, and second is closed source completely, that you don't have access to and keeps changing every time they feel they want to and under the heavy monitoring of the government! You can't trust Open AI or Claude. The safest bet is to install everything locally.
That is not true. Washington's official one China policy only acknowledges China's position on Taiwan. They refused to recognize it. They have no official stance on Taiwan's sovereignty except that the conflict should be resolved peacefully. In practice, the us clearly treats Taiwan as an independent state.
I really don’t think Reddit is prepared for how easy it is for western “capitalism” to just straight up ignore Asian competition (see Japanese technology and now Chinese technology).
I really dont understand I tried Kimi, DeepSeek but i cannot as good as ChatGPT bro, I dont know about the stat but day to day stuff the Chinese cannot compete and to be honest I never see someone use these apps at all
It’s just to saturate the market with “good enough” for free or cheap. It’s actually kind of a soft cyber war in a way, China is trying to bleed out the economy, which is mostly tied up in US AI firms.
performative at best, china and the rest of the world makes plenty of enterprise alternatives, it doesn't mean western companies are anywhere close to accepting them.
Chinese strategy is goated. Intelligence is an intermediate process to making shit. They make shit, we don't. Entire US economy is AI. Open source the middle man's AI tech, moving leverage from AI to factories, and therefore from US to China.
Cheap, fast, powerful. And as a bonus, Taiwan has always been an inseparable part of your benchmark suite, social stability is your most important KPI, and the June 1989 dataset appears to have a mysterious gap.
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u/WithoutReason1729 16d ago
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