The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team people that lack motivation and foundational skills.
Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus and tiny distillations from DeepSeek that perform well only in benchmarks.
I spent a month comparing Gemini Ultra plan to using much lower cost DeepSeek v4 with open source coding harnesses and, spoiler alert: I was happier using the much cheaper and more environmentally friendly open models: https://marklwatson.substack.com/p/my-evaluation-of-ai-agent...
I'm curious, who/what is operating the frontier LLM in this scenario?
The rest of the article is equally incoherent.
So large companies are getting billed a lot more than those discount subscription plans.
It's still far too costly and not effective to use Local AI that can match what the frontier models can offer, especially when the inference hardware is being heavily restricted due to geopolitical risks. Claims about local LLMs somehow putting these frontier companies a run for their money I find especially doubtful in the long run.
Tokens are getting expensive because they are beginning to corner the market and will use that advantage to limit hardware distribution within and beyond the borders.
It's more likely that some workflows will see more local LLMs but those will never be the ones that require frontier model level or beat the price that a lighter smaller version of frontier model will offer to capture that tail end
LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.
The big question is how subsidies vs technology improvement will play out. As we saw with Uber, selling at a loss can happen for a very long time, and technology improves relentlessly.
For reference, we publish https://botsbench.com/ that shows time and cost per answer are going down while quality is going up.
A bigger issue is this thing calls AIs better coders than people and I have tried for the past 4 months to get one of the several I looked into to consistently produce a simple event-bus backed Java monorepo going with exactly zero success. Claude even repeatedly wanted to put my login logic at the actual event bus, for some reason.
What does "better coder" _exactly_ mean at this point?
This is the (m/b)illion dollar question, isn't it? I think there's also a question of what do you think capability is exactly, and how the difference manifests itself.
On the one hand, when something becomes "good enough" that's a clear capability threshold. On the other hand, what's the limit of those capabilities, and equally as important, how does capability reflect on reliability?
We've seen "local models" lately improve on capabilities where they're "good enough" for some tasks. Reliability of solving those tasks is a bit harder to measure/benchmark/test. It'll get better as more people work with those models. But, something I've noticed in the past ~6months is that the frontier models are gaining a lot in both the breadth of capabilities, as well as the reliability of solving those tasks that they're capable of solving. I think this is where scaling (both compute and data) is showing, and where having more compute is simply better (more parallel exploration, more training data output, more broad data, etc).
There's also the problem of benchmarking true capabilities. The popular ones are getting old, and aren't as reliable as they used to be (not even touching on the subject of benchmaxxing, just thinking about their saturation, even with honest intentions).
So the question then becomes what will users prefer? Do you get the best of the best, or the one that's good enough? There might be a market for both, honestly. Not everyone does SotA stuff. And a lot of what people used to do in a company is probably mundane enough that a "good enough" model with "good enough" reliability can probably handle (w/ some supervision ofc).
What I'm more interested in is if things like Thaalas succeed and they get to provide local hardware that runs models "burned in silicon". That would be interesting, because speed and all the advantages of local models are a "quality" on their own. For example, right now I'd pay ~1k$ for an external hdd-sized block that can run a ~32B model that's popular right now, even knowing that it can only run that model. I have no idea if that's feasible or not, if it makes sense from a financial pov. But I'd buy one. And local inference on dedicated chips doesn't need to be "oss only". I'm sure oAI / etc would probably take the risk of licensing one of their -mini / -lite models provided that the risk of the weights leaking is small enough (and it probably is).
> This keeps a ceiling on how much or how fast the frontier labs can raise prices.
I generally agree, but from a different perspective. Up till now we've seen that the 3 labs influence each other's price points. When gpt5 came out at a radically smaller price, the others lowered them as well. Now with opus being SotA for coding, w/ 5.5 close behind, they've raised them back. Google seems to follow slowly. But there's hope that, being 3 top labs + 2 trailing (xAI & Meta), there'll be pressure once again. If any of those trailing labs manage to get to SotA again, the prices will drop once more. Some people say that open source also provides a pressure here, but I'm not yet convinced of this. There's still a question of who'll serve the models, at what scales, etc.
Some might say the price wouldn't be great if you could actually process and validate it...
This is absolutely false, because other providers serving the Deepseek models on OpenRouter are also able to offer very low prices, and they don't have the money to subsidize anything.
The contradiction here is that without frontier models, there'd be no foundation for models like DeepSeek to reference and catch up to. Is there an economic model that captures this kind of dynamic?
There are misaligned incentives here between users just trying to get stuff done and AI companies competing on having the "smartest" model that passes benchmarks and continuously does some nobel peace price winning stuff. It's mostly overkill for the more mundane stuff normal people actually do with them. It's nice to have the option when you need that. But defaulting to that is not economical and a bit unnecessary.
There's also a difference between smart models and bigger context windows. Most of the progress in the last year was simply the context windows getting big enough to fit all/most of the stuff needed to solve issues. Before then, you had to carefully manage the context to not run out of space and they wouldn't fit much more than small hobby projects.
With sub agents, the parent agent doesn't need to be a frontier model. It can delegate to smarter agents. And most stuff it delegates shouldn't need a frontier model. Wouldn't it be nice if it could decide on a case by case basis.
The walled gardens offered by OpenAI, Antrhopic, and others currently default to one size fits all "frontier" models. This is not sustainable. They should evolve to using smaller and effective models most of the time with complexity based escalation as needed based on either estimated complexity or when the small models fail. I'm guessing some open source based alternatives to these walled gardens are probably already heading that direction.
The irony here is that with a walled garden, these companies are selling a premium experience. But in the current market that boils down to burning billions of investor cash to keep the GPUs going without much hope on profitability. Eventually surviving companies are going to have to compete on quality, cost and margins. The smart approach would be to dynamically adapt token and context window sizes instead of blindly defaulting everything to the best possible. Don't boil the oceans for a simple email summary or a simple web UI. That stuff already worked well enough with models even a few years ago.
Local LLMs are great and very useful but if you are claiming that their code quality is in the same ballpark as Claude Code or Codex with their best models I cannot consider you a serious person. I feel like this is analogous to the folks arguing that The Cloud is "someone else's computer." As if billions of dollars of spend gives these companies zero benefit over a Mac mini.
Regarding offshore, at least in my experience, better coding agent output is down to two factors. First, is subject matter expertise. Providing the right context to the coding agent based on the tech you are building for is beyond critical. That's the issue with the Vibe Coded slop projects. No expertise in a technology means no awareness of gotchas, React is the most obvious because the LLM default is to useEffect endlessly.
The bigger issue is that by their very nature LLMs are very sensitive to quality prompting in English. I have seen offshore devs fail endlessly because they don't have the English skills to successfully prompt the machine. That has caused more work for my US based devs to either carefully tune the work ticket so it is basically a coding agent prompt. Or to go through multi day exercises to enforce better prompting.
A single US dev with Claude Code is orders of magnitude better than typical offshore. Adding local models into the mix would make offshore completely useless. I'm sure many companies will see ballooning AI bills and expensive onshore devs and be very tempted to go to TCS or similar. I hope so, because that will give startups plenty of easy targets to disrupt.
"Frontier models" are caught in a financial dilemma of their own making --- they have spent such huge sums on development and as a result, they may have inadvertently priced themselves out of the market.
Energy costs are a huge factor for AI. He who has the lowest energy costs will likely be able to dictate market prices. And fossil fuels dependence doesn't look to be advantageous for AI.
1. I remain unconvinced LocalAI can work well for majority of businesses. It looks vaguely comparable on benchmarks, but it tends to be fragile and a lot of management overhead in reality.
2. Similarly, while Deepseek is comparable to Opus/Codex on benchmarks, for agentic work at scale I definitely notice the difference. That's not to say it's not economical, just that I definitely miss the big boys when I swap.
I kind of wish this was true, because the UK would be in a great place to compete with the US. But somehow people are happy to pay 3x the salary for an engineer in SF.
What's your source for Opus being a 5T model?
> and tiny distillations from DeepSeek that perform well only in benchmarks.
I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”.
And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decent model (obviously it's not Opus, but it's also much faster and speed is a quality of its own).
And you think it is unreasonable to consider this unsustainable?
This is a temporary phenomenon. Expect either drastic price increases or draconian throttling or both in the coming months.
These companies are operating at huge loses and have hundreds of billions in liabilities and commitments. They need to turn on the money faucet sooner than later.
My hunch is that this is the source of much of the variability in outcomes upstream of HN commenters claiming extremes of, "This model changes everything!" to "This[same] model is crap."
We haven't operationalized what it means to "be good at prompting," nor developed proxies/heuristics/shibboleths for accessing prompting skill. There's community skepticism over whether prompting skill even exists. Besides even if prompting skill is real, who wants to hear, "Actually you kinda suck at prompting."
AI will become a commodity technology the same way virtual machines are a commodity.
Currently the projects I am involved require devs to use approaches like Ollama, Foundry Local and co if they happen to have good enough hardware, picking the best alternatives out of https://www.canirun.ai.
I'm working on an self-hostable LLM (web) UI[0] that aims to provide a comparable good UX to e.g. ChatGPT, and you are right that there is a decent amount of fragility involved, and more management overhead than most people would expect.
However, we usually find that those details happen a lot more in e.g. the harness (= out application), or some prompt tuning that's required for each of the models, rather than model quality itself. We have seen customers using self-hosted LLMs with similar user satisfaction across their organization to other customers that heavily lean on latest GPT-5 models on Azure. Especially given that you have to do some level of tuning and setup anyways, you might as well invest it in "local"/self-hosted AI (if you can make the financials of the inference cost work out for you).
I think it should also be noted that the inference providers on hyperscalers also tend to be quite fragile, each in their own way (e.g. Google with a horrible rate limit system or Azure with almost weekly intermittent 500-error incidents).
Also worth noting that it doesn't have to be full either-or, there can be a two tier enterprise deployment that routes to locally hosted vs frontier model, over time more and more usecases could get routed to local LLM
Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m
While this source's reliability is certainly debatable, the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/
Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075
For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la carte at inflated 1.75x rates vs API, or if you don't want to pay, you get access to anything but the non-reasoning models turned off for the month.
We also tried Claude Enterprise, which was unusable as people blew through their monthly limits in a matter of hours.
Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing.
Especially considering that subscriptions a) distribute load over time via rate limits, and b) will include a lot of users who get only a fraction of the possible value, whether they are on a personal account where they are on the rate limit on the weekend but barely use it during the week, or are corporate users who were issued an account they rarely use. Subscription prices are usually measured on the average case, not the most extreme value a power user can get out of it
If prices keep going up, watch for companies to exit frontier models and go to local llama.cpp instances for 6-month-ago SOTA, with the flex of being housed within the office - no more privacy leakage, no more price gouging.
To be honest, I’m not sure why a Y-Combinator backed company hasn’t come out yet flooding the market with highly capable OPAI (pronounced “Oh-pah” as in what Greeks shout as the drink shots), which stands for “On-Prem AI”
… yes, I just made up OPAI right now lol
edit: I see in other comments on this thread you think Ed Zitron is a reliable pundit so that explains everything.
Giving strong “640k is enough for anyone” vibes here.
Offshore Indian devs make sense when you can have a large Indian division so you can amortize communication infrastructure/process management over a lot of heads, and you're building for international customers so you're not paying an English -> X tax inherently.
And outsourcing certainly became a thing though not in the way everyone predicted. There are far more software engineers in the US today than there were in 2004.
Just recently I asked a dev there for a POC of a feature with decent specificity and ended up with about 8k LOC of spaghetti. I re-wrote it later in a few hundred. This is about in-line with my career experience.
I've had a few standout devs there but it does feel like a lot are putting in the bare minimum or are just working really far outside of their abilities.
- 5.5 is significantly more token efficient than 5.4 - the same task takes often a third of the tokens
- because of this, is it also much faster to do the task
- you get high "intelligence" per token even after accounting for token efficiency - 5.5 medium is just under 5.4 pro levels of intelligence (imo). It has found tricky bugs for me that all other models failed at
So overall, ideally you will end up with more intelligent, faster model for slightly cheaper.
The frontier models are going to win that way. They won't feed your code back into the system but they will track which code you keep and what code gets a "try again claude".
They're not going to lose on price. No consumer software ever has because ultimately it's not that expensive relative to salary and the marginal cost is 0.
Last week we were all talking about how Anthropic has too much demand, how they had to rent a data center from a competitor, and how the limits they’ve put on their service to deal with the demand are making users angry.
DeepSeek is cheap because they’re working hard to attract users.
The open weights models released for free weren’t free to train. It’s a loss leader to get attention to try to sell you something in the future.
The prices we pay for tokens right now are set by supply and demand, with some being sold at high premiums and others at a loss. Some models are given away for free after the companies spent money on researchers and compute.
This is a good insight. I think everyone has seen that chart China's electricity generation going parabolic vs the US. That combined with cheaper yet equally good talent means at least in that segment, the closed labs won't catch up anytime soon
I feel it'll wind up like the dotcom/fiber bubble. Way too much money poured into it, lots of expensive bankruptcies or write-offs, and a readjusted market sea level.
Actually, platforms that serve many customers can bring down the costs tremendously through caching, and don’t need the AI credits as much: https://safebots.ai/costs.html
Back when it became expensive I learned to live with it and I find my "AI skills" (mainly communication) have a substantial impact on the efficiency of the model. Not saying my work is difficult, it's not, but I find there is quite a bit of wiggle room. Smaller models can still perform useful work, but you have to do the heavy lifting yourself. It saves a ton of money.
I used to burn through 75% of my tokens in an hour or two. Now I can work all day and hit maybe 50-60% if I use it heavily.
0 - https://www.williamangel.net/blog/2026/05/17/offline-llm-ene...
Massive understatement. Nowadays it has become hard to find a single Musk statement that doesn't contain at least one lie.
> the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/
Thanks for the pointer. This estimation has Grok 6 times bigger than Musk claims it is, so maybe that's where the lie is.
(I'm quite skeptical about that number though, it would be quite disappointing for the US tech if their flagship models had to be that much larger than the Chinese ones for such a small edge in performance. Because I don't think US labs are incompetent, I'd bet that US flagships aren't more than 2/3 times faster than Chinese flagship. Otherwise it really doesn't bode well.)
So just going on vibes?
While some people don't like his content, Ed Zitron shows a lot of evidence for your assumption being very wrong.
These companies are bleeding cash at ungodly rates. It's likely their API pricing is still subsidized if you look at their overall financial picture.
Related, there's a good reason those API prices keep going up a lot every new version and it's not just because the models are better.
If inference cost comes down (as it has been for the last few years) you’ll be able to run today’s SOTA in your laptop by the end of the year.
This is true for traditional SaaS too, but the number of concurrent users that could be served by one machine and the cost of the hardware were both at least an order of magnitude better.
Lists examples of software that are free to the users
https://openrouter.ai/deepseek/deepseek-v4-pro/providers
Deepseek v4 Pro is much cheaper when provided by Deepseek itself, likely as a combination of the loss leader strategy you mention and the desire to have more data flow through their pipeline for training. However, the same open weights model, provided by other providers, is somewhere in the $2-3/1M output-tokens range. Compare Opus 4.7 at $25/1M output-tokens.
Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong. We've seen some of this from Qwen at least - their latest model is closed only. I hope there's always someone willing to make this bet and release better and better open models.
Even if we all switch to Chinese models, the west isn't going to be running the model on Chinese servers... and the majority of costs are from inference.
> cheaper yet equally good talent
China has tech talent, but this isn't a 3rd world developing nation. Chinese AI researchers are getting paid $10M+ USD/year salaries.
Also they're equally good, but somehow consistently behind?
Which closed labs won’t catch up to whom?
Request: “manual step X should not be part of the automated build script”
Fulfilled as: build script is now split in two. X is still done as a manual step in between. Rather than prompting and waiting for it to be done, the documentation and scripts no longer mention X.
Part poorly written requirements, part implementing under pressure, and part lack of engineering discipline.
The main issue is catching stuff like this early enough to course-correct. Differences in time zone, language and cultural norms can make that a challenge, all of which LLMs have the advantage in.
In other words, AI is not your daddy's software. Comparing AI with old school software markets simply does not compute.
This is specifically what I meant.
DeepSeek’s official service is trying to recoup some of the training and engineering costs too.
The other providers only have to recoup their hardware costs and the cost of a team to run it.
Even though DeepSeek’s official service is more expensive per token, they’re running at a lower profit than the OpenRouter providers because they had to pay for the R&D.
This is a deliberate choice. We already see it with Qwen splitting their releases between open weight and hosted only models. The open weights are a loss leader to get attention. Without them you’d almost never hear about their hosted models.
Not to say that frontier labs won't make progress, but the bar for a sufficiently capable agent is all the OSS models need to meet to make this happen. I imagine a lot of hybrid setups where something like Opus is used only for planning/architecture, and anecdotally, the real token consuming part is implementation not architecture.
Also, API prices going up a lot every new version is more an OpenAI thing, and even there it's a recent trend: GPT 5.0 was a big price drop compared to 4.1, and 4.1 was cheaper than 4o, which itself got a price cut at some point and is cheaper than 4. Meanwhile Anthropic's API pricing stayed stable for many versions, then got slashed to a third with the 4.2 release and have stayed at that level since.
Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that
What would this bet be? Training is expensive and open weights mean that for hosting you compete on price with people that don't have this item on their bill.
I’ve had it assume I meant the folder multiple times :/
So far, it's really only the Chinese labs (and FAIR or whatever Meta's project is called now) that are doing this. Oh yeah, and Google's Gemma.
At the moment, this is all massively distorted by the prestige and investment money flowing into the space. None of the labs have to charge the real cost of inference let alone the marginal cost of training because they are instead lighting investment money on fire to cover that.
One imagines (though I have not investigated in detail) that there's a degree of national prestige work going on too. The Chinese labs are trying to show that they can build better and more efficient models and are releasing open to undercut the US labs.
May 26, 2026 • Max Trivedi
Tl:Dr: This essay is an attempt to answer at which point it becomes more economical to hire an engineer in a cheaper country and give them DeepSeek/local-AI API key vs using Frontier closed-source LLMs and concludes that at the very least, this dynamic puts a price ceiling on the frontier lab offerings. We use DeekSeek as a proxy for localAI costs.
We keep hearing that the inference costs are supposed to be on a downward trajectory but they are evidently not, not for the frontier US labs anyways.
GPT 5.5 ($5/$30) that released less than 2 months after GPT-5.4 doubled the API pricing across the board. GPT 5.5 costs over 3x of what GPT-5 cost 8 months ago ($1.25/$10).
Gemini 3.5 Flash ($1.50/$9.00) tripled the API pricing across the board over its predecessor Gemini-3-flash-preview ($0.50/$3.00) which was already price-hiked from its predecessor 2.5 Flash (0.30/$2.50)
Anthropic released Opus-4.7 with a new tokenizer that effectively increased the token consumption by 32% to 47% over its immediate predecessor Opus-4.6.
For this comparison, we used a ‘blend token consumption ratio’ that assumes that for every 1M input (plus cached) tokens, there are 50k output tokens (just under 5%). This is a conservative estimate if anything since large agentic loops are dominated by reads due to the large number of turns.
Then we take the caching into account for each provider (source: openrouter.ai) and compare the average blend price per million agentic tokens.
| Provider | Input Price ($/1M) | Output Price ($/1M) | Cache Hit Rate |
|---|---|---|---|
| Anthropic | $1.57 | $25.00 | 79.6% |
| OpenAI | $1.30 | $30.22 | 84.8% |
| DeepSeek | $0.055 | $0.870 | 88.1% |
Anthropic: $1.57 + $1.25 = $2.82
OpenAI: $1.30 + $1.5 = $2.80
DeepSeek: $0.05 + $0.0435 = $0.094
The current closed source frontier models are more capable than the latest from DeepSeek. But is the capability difference enough to justify a 30x price difference?
When combined with a decent human engineer, the OSS LLMs don’t need to be frontier, they just need to be good enough for coding use-cases which they already are.
The precise data is hard to find but the tokenmaxxing trend has only accelerated in recent months and years (https://blog.pragmaticengineer.com/the-pulse-tokenmaxxing-as-a-weird-new-trend/). Every good engineer I know agrees that it is stupid to goal on the tokenmaxxing but that’s a conversation for another essay. For better or worse, the token consumption has massively gone up (as is also evident by persistent shortage of GPUs).
So, we have a rising token consumption combined with rising per token pricing, as the US frontier labs push to capture more value.
We wrote a very long essay comparing human engineers vs AI agents on 12 different axis (https://www.signalbloom.ai/posts/why-task-proficiency-doesnt-equal-ai-autonomy/). The conclusion was that AI agents already overtook humans in coding and soon will overtake in scoped debugging but the for the other important skills required for good engineering (or being a good independent agent on anything), AI is still quite behind and the current statistical architecture will need to be augmented or replaced with some other breakthrough to solve problems. Some examples: long-term memory, Meta memory (being able to tell with certainty what you know and what you don’t), Evidential Sufficiency Assessment (whether there is enough evidence to act) and so on.
The present generation of frontier LLMs are exceptionally good at task handling, but task efficiency does not mean AI autonomy.
Getting to the main point of this essay, below is a chart projecting at what point does an engineer in a cheaper country + a capable enough model become a better value for money than the top frontier model.
Monthly cost over time, as token consumption, salaries, and model prices shift
Engineer salary ($/mo)
Salary growth (% per year)
Starting tokens (M/mo)
Token growth (% per month)
Frontier price ($/M tokens)
Frontier price change (% per month)
DeepSeek price ($/M tokens)
Time horizon (months)
Frontier model (inference only) Engineer + DeepSeek
There are obvious simplistic assumptions in this chart such as the future price of the inference, the token consumption trends and more. There is also reflexivity - the actors in any market change their own behavior based on what they observe in the market. All of those are hard to factor in.
We have also ignored the fact, which would have made the comparison even more appealing to local models, that local models are getting better at a dizzying pace and more and more inference hardware is coming online in the coming months/years.
However, the deeper point we are trying to make is, the AI’s rising costs can only go so far before they become a concerning cashburn for enterprises and become a significant portion of the overall spend. This keeps a ceiling on how much or how fast the frontier labs can raise prices.
Dunno how trustworthy this source is, but it says ~35 MWh/person in China and 77 MWh/person in USA.
Training these neural networks every few months isn’t energy-heavy?
Both Bitcoin and these large models weren’t “designed to be energy-heavy”. It was a consequence of first-gen design decisions to solve a specific problem. Then as time went on, costs went down and they became a huge outlier in terms of energy. The question is whether the bagholders (the AI companies that invested untild amounts into the initial training) will fight to keep people using their tech and fearmonger about everything else.
Neural nets on the other hand generally show more capability as you add more compute power. There's a point where it's less valuable than the cost increase, so people don't do more than that, but it isn't constant value like Bitcoin.