https://village.dev/blog/closed-ai-models-now-dominate-the-p...
That's $104/day of opus 5.5 tokens, or $265/day of fable 5.1 tokens.
If you were able to saturate usage of this system, you could pay it off in 3 days of fable time or 6 days of opus 5.5.
But ok those are more intelligent. Let's go with something that scores similar - sonnet 4.6. 5.2M tokens is $78/day and the system is paid off in 8 days.
But you’re not generating Opus or Fable tokens.
And people buying subscription plans aren’t paying API rates.
I have a Claude subscription and I have hardware to run Qwen3.8-Flash-Next a full order of magnitude faster than the theoretical system you specced above.
In no way would I ever consider my local Qwen3.8-Flash-Next setup comparable to even a $20/month Claude subscription. It’s just not.
I do try to use it for bounded tasks from time to time, but using it for actual complicated work generates so many problems later that it’s not worth it.
Often I can have Opus produce a better result in 1/10th the tokens. The Qwen model often gets things wrong that require 10X as many tokens to debug or fix later. You cannot make comparisons like you’re trying to do.
In my opinion, what we actually have to compare against is the $20 subscription compared to what using Claude or ChatGPT is like with the free plan.
If I use Claude free it cuts me off from using it for hours after a very small number of questions being asked, and it can’t be hooked up to a coding system at all (no Claude code CLI or VSCode extension).
So when I dropped a $500 GPU into my gaming server in my closet, I’m breaking even compared to the $20/month Claude subscription in 2 years (and my GPU already appreciated in value since I bought it).
I can send as many queries as I want in any way I want, including custom applications that would have normally been billed at API token rates.
On top of that, all my data is kept private to my server.
(I’m not counting electricity because my server was already on serving all my self-hosted apps, so a significant amount of electricity is only being added to the load when I’m actively asking questions)
No, we’re not going to compare $500 GPUs to free tiers.
The contorted logic in these threads makes my head hurt.
> So when I dropped a $500 GPU into my gaming server in my closet, I’m breaking even compared to the $20/month Claude subscription in 2 years (and my GPU already appreciated in value since I bought it).
But the $500 gaming GPU in your closet isn’t providing Claude level intelligence or performance. Not trying to brag, but my LLM hardware is an order of magnitude more expensive than that and I hold no illusions that it’s comparable to what I get from Claude.
If you want to compare against something, compare against Qwen tokens purchased through a provider. Compare like for like.
The $20/month Claude sub is on a different level of performance entirely. There is no direct comparison on quality or speed until you’re spending A LOT more on hardware, and even then the closed models still win.
The free Claude subscription is an insufficient level of service because what I'm looking for is the ability to send as many queries as my heart desires, and to be able to use it with a coding agent. Therefore, I must spend some kind of money. The cheapest flat price plan I can buy from Claude is $20.
That's why I'm comparing against $20 a month. Of course, maybe for this sparse use case it would be better to use API billing instead of a $20 plan, although maybe that means I'm thinking about billing variability.
I am not comparing it to the level of intelligence that Claude offers. I'm comparing spending $500 one time to spending at least $20/month forever. Yes, it's smarter than the model I'm running, but if I'm not paying that money Claude ends my usage mid-conversation and tells me to come back in 5 hours.
The model I'm currently running is Qwen 3.8 Flash Next using Strata (https://github.com/Niko1221/Strata), the VM has 64GB of system RAM assigned (which was previously sitting unused, with deep apologies to Ram Altman). It's definitely not a bad model, and gets the job done for me.
I would generally agree with you that I'm never going to break even against buying tokens from better open weight models on the open market, or maybe even just buying Sonnet 5.5 tokens, but I'm also gaining the data sovereignty aspect, which is worth a non-zero amount of money.
On top of that, the GPU is an asset with resale value, so I'm not losing the money permanently. It has appreciated in value by $150 since I purchased it, and will probably be worth no less than $200-300 in the next 5 years in the worst case.
Doesn't matter if it's free, you'll spend 3x the time correcting its mistakes.
I think most people unfortunately don't care about any of those things, or at least they don't care enough as we are about to see by the trillion dollar IPOs of these AI megalabs.
Anthropic continues to serve haiku - which suggests people have a use for less-than sota models.
A bugatti is undoubtedly a higher performance car than a honda civic. Yet if you're commuting to work in rush hour traffic, both cars will get you there in the same amount of time - and one is going to cost you a lot less.
The pitch of the frontier labs is that you need a bugatti for everything. I don't buy that.
1 qwen output token != 1 opus output token.
Yes I know what the benchmarks say. It does not reflect my experience.
You’re also comparing benchmarks for the full model, but then telling people to run IQ3_S. I found the IQ3_S model to be a substantial step down from 4-6 bit quants.
> You’re also comparing benchmarks for the full model
I wish there were more concrete ways to judge the difference. I've spent a bunch of time using 3 and 4 bit versions of qwen 27b, and my conclusion was that the lower qaunt made more mistakes - but that both systems were able to recover from those mistakes in a coding context. I'm not sure if that holds across the board, but that's the best answer I have so far.
It's also unclear if anthropic always serves you f16 versions of their models. My friends who use claude a lot complain about intelligence that varies from day to day - but that's entirely anecdotal.
If all you do is generate tokens all day, sure, host your own infra. For people that aren’t maxing out hardware, using someone else’s is a pretty economical option. $20 with deepseek flash gets you a LOT of agentic use, and as the frontier moves forward with new models, so do I.
But let's compare apples to almost-apples! Qwen3.8 flash next is $0.47/M on openrouter, so $2.44 per day, so 246 days to break even. I say almost-apples because on this $600 system we're running an IQ3_S quantization. The hosted model is running... nobody knows what quant. It probably varies bases on the time of day.
Also, if I wanted to generate Sonnet 4.6-quality tokens, maybe your suggested setup is the better approach (though I still highly doubt it), but I don’t. I want Opus 5.5 / Fable 5.1 level of quality, because the ROI in terms of human-in-the-loop time saved and how much better and faster I can build is worth WAY more than $200 / month to me. That’s basically a rounding error for my business.
Anyway, if it’s worth more than $200/month definitely don’t spend $600 and expect it’ll replace that. But $2400 ($200/month for one year) gets you much closer to sota than $600 - and whatever the line that stops being a rounding error for your business gets you closer still.
How much is it worth to own your livelihood? To me outsourcing to anthropic or openai for $200/month means you’re handing them the ability to kill your business at any moment without even noticing. That’s worth more to me than 30% points of intelligence.
You clearly have an ideological issue with these companies, but I don’t share your views or your values. They could cut me off tomorrow but it wouldn’t kill my business. Worst case, I’d have to figure out how to make do with what you’re already choosing to do today.
That's just the issue of your access to AI in the worst case.
The actually impactful part of the worst case would be what happens to your ability to have a job, put food on the table, or even afford to live in a house.
One of my beliefs, call it ideology if you like, is that api cost is much more representative of real cost of this stuff. Good luck switch to local models the same week everyone else decides to switch because subscription prices went up 5x… it’s going to cost more than $600.
Anyway, like it or not, you are giving $200/month to fund another ideology. One that looks to me like a leopards-eating-faces type of ideology.
But in this case it seems like it's led your reasoning astray into this perspective where a small open weight model you can run on cheap hardware is just as good as the value you get from paying $200 to Anthropic. The reality is that it's not even close, at least for some people's use cases.
I really doubt I'll ever need to use local models, even in a scenario where the big labs stop doing subscriptions (unlikely), I could just pay API rates for the open weight models through OpenRouter, or just pay for API rates for Sonnet 5.5 or GPT 6.1 Sol, and adjust my workflows a bit. Still worth it.
I don't know, there are a LOT of things I worry about with AI, but what I'm going to do when the big labs drop subscriptions isn't even in my top 10. Probably not my top 20. Just a non-factor.
As long as you have a business. AI will "Sherlock" many and commodify all.
I don't really understand the argument being made here. The best models are better, but they're not that much better. If I'm paying only 20% as much for 20% less performance, I can make up the 20% and have abundant cash or compute left over to spare.
The other thing that matters to me is the tooling. I could use Deepseek Harness with Claude but I'd rather support the same people that brought me DSH.
As a consumer I don’t care. The price I pay is how much it costs.
I’ve been hearing arguments that they could increase the price at any time. For years.
Instead the opposite has happened: A $20/month subscription has become more capable than it was 6 months or a year ago, by a huge margin. I can get so much done within a day of the $20/month subscription that it’s hard to believe it’s so cheap.
By the time they reach this point of raising prices, if they ever do, the cost of models with current intelligence levels is going to fall a lot.
So all of these arguments about buying hardware now and switching to open models now to prepare for future price increases are still wrong if they’re argued on the basis of being locked out of cloud intelligence costs.
The cost for a given unit of intelligence is only going to decrease.
Seems more like a final fight against open weight models than a sustainable business model.
Seeing comments about the major AI labs breathing their dying breathes because they have so much debt is really fascinating. The ideologies have started to exist in their own little bubble divorced from reality.
Certainly I see the benefits in terms of privacy and security. But if you're trying to justify local models on economic grounds, you also need to factor in electricity costs.
(Also, people will say that "less than a year ago" represents an enormous capability gap. But people were already ranting and raving about how capable the models are and how cooked the industry is, considerably longer than "less than a year ago"; so.)
But ok, let's compare: https://benchlm.ai/compare/gpt-6-astra-vs-qwen3-8-flash-next
Astra's score: 85.8/100
Qwen3.8-flash-next's score: 55.87/100
So there's a ~30 pt difference between them. Let's add 5% more for quantization damage.
My perspective is, yes - that's a huge difference. No - it doesn't matter. Flash-next is above the threshold of useful intelligence. I can ask it to do something and it does it. Sure, it burns some tokens on bad ideas on the way, and sure it can't turn a single paragraph prompt into million dollar business. I don't need or want that - I want something I can ask to refactor a module, and it does it and it doesn't stop until the module compiles and all the tests pass. flash-next does that.
Don't have to be. They just have to be good enough for the tasks you need.
I use both models extensively and there is no way I would ever agree that Astra and Qwen3.8-FN are that close in the real world.
I agree with the other comments: These comparisons are silly.
You keep saying that you don’t understand why anyone would have a Claude subscription and reciting ideological reasons not to. I’m assuming this means you don’t use their new models much? That’s the only explanation I can think of for how someone could think that Opus 5.5 or even Sonnet 5.5 and Qwen3.8-FN are close.
I use them both (often Qwen3.8-FN as a subagent) and any claims that they’re actually pretty close read as downright delusional to me.
Putting aside the absurd straw man, you’re touching on an important point: these small open weight models are sufficient for how you like to work, but it’s not remotely good enough for how I like to work. Opus 5.5 is barely sufficient for how I like to work (many parallel work streams, as little of me in the loop as possible, etc).
So not really work, then?
You’re automating yourself out of a job using capital you don’t own, and you think your position will hold? I am skeptical.
I work like the parent poster does. I am working harder than before the AI tools came around. I am just working at a higher level. Not too dissimilar from someone moving from programming in assembly language to significantly higher level language.
> as little of me in the loop as possible
You may be confused about this. If I am running just one loop at a time, you'd be right. What modern tools allow me to do is think more like a supervisor that has dozens of engineering teams available to him. I always have a long list of problems I need to solve. I used to sit with one and bang on it until it would pass acceptance criteria. Sometimes that took months. Now I just farm out each task to an agent orchestrator, provide it details on what I want done, a bit of design and architecture and acceptance criteria. I then step away from it and move to the next agent orchestrator and repeat the process for the next task. I usually have 6 or 7 large tasks being worked on at the same time. This is currently my personal limit. I can't babysit more than that. But as the models and harnesses get better I suspect I will have to do less babysitting and this process will get more autonomous.
It may sound like this process would remove the need for engineers. In my organization it is doing quite the opposite. As the organization figures out that we can do a lot more, we change our plans to build more. A lot more. Building still requires engineers, more than before, for us. But only those who can think like architects and can manage large teams of AI agents and orchestrators that work 24x7.
And I work for myself, so no, not automating myself out of a job.
Although I suppose I’m automating the people I won’t hire now out of a future potential job.
I run my own local models, and I can tell that yes, one ask and they do... with many more errors, and much much slower, compared to the lowest levels of frontier models.
Sure, if one has no standards at all, local LLMs definitely are "above the threshold of useful intelligence".
I'd rather wait all day for an impeccable implementation of a single feature than get 10 features worth of "okayish" code
I have hardware to run Qwen3.8 Flash Next (the model you used in your example) much faster and closer to full quality.
I do not understand how anyone thinks it replaces a $20 Claude subscription in performance or quality. At best I some times have Opus give well defined tasks to Qwen3.8, but I have to be very selective about what is handed over. I also use a lot of Opus tokens reviewing and fixing it.
For real work (not fun projects or experiments) I won’t even bring the Qwen models in to play at all. The gulf between Opus and Qwen3.8-Flash-Next is that big.
So I hope everyone takes comments like this with a huge grain of salt and healthy dose of skepticism. If your work has a high tolerance for slop and you can wait a very long time, the local LLMs can do a lot.
However claims about replacing a Claude subscription with Qwen3.8-Flash-Next? The only way this makes sense is if someone is choosing on ideologies like privacy or avoiding big corporations first, with quality a distant second.
I get downvoted every time I say this, but it’s objectively true. The open models that can be run on consumer hardware are far behind Opus, and quantizing them to Q3 takes them even further behind than the benchmarks show.
Also 100 tok/s prompt processing and 20 tok/sec is painfully slow for real work. The slowness is multiplied by how much extra work the less intelligent models have to do to get tasks done.
I want more than "functional"
In fact, I just switched from a 96 GB ram laptop to a 32 GB ram laptop specifically so I can have GPU acceleration because 20 toks can’t replace the 6000toks or whatever astra does
Not today. But when it turns into a $600 brick you plug in next to your router, that's gonna work for most people.
For today, I'm speaking to software developers - who can 100% handle setting up a local AI system - it's only as hard as building a PC.
I asked an AI agent to setup local AI inference for me. I gave it my use cases. It downloaded all the required bits, installed and configured them. Benchmarked different settings and models for my hardware and my use cases and then produced a nice report with findings and recommendations.
It is significantly easier than building a PC.
I know that configuration works, because I am running that configuration right now.
The whole AI world is run by sociopaths that want to make human brains economically uncompetitive comparedto computers. If you think running local models will save you, you're either very pessimistic about the future of AI, or you haven't thought it through.