When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing.
The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team people that lack motivation and foundational skills.
Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus and tiny distillations from DeepSeek that perform well only in benchmarks.
> When discussing LLM pricing, people are missing the plot. [ ... snipped ...] Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing.
And you think it is unreasonable to consider this unsustainable?
And the direction is definitely towards removing that subsidy really soon. We can see it with OpenAI's shift to API-equivalent pricing for enterprise customers last month. Anecdotally my company saw OpenAI credit usage grow 2x with stable use across the ChatGPT platform, which is pretty terrifying considering just 2% of the company uses Codex. (For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la cart at inflated 1.75x rates vs API, or get access to anything but the non-reasoning models turned off.)
Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be.
I learned today that the Anthropic "Enterprise" plan - the one big companies use because they need governance features and audit logs and all of that jazz - is billed at API token rates (plus $20/seat/month).
So large companies are getting billed a lot more than those discount subscription plans.
Isn't the plot that it's like an infinite bikeshed but 10% of the biksheds are actually trailer parks and when you finally realize it's a trailer park and not a bike shed you're down 10-100$ because it's token gen is faster than you can actually validate?
Some might say the price wouldn't be great if you could actually process and validate it...
I think this misses the forest for the trees. Working with ChatGPT is eerily similar to working with offshore Indian devs back in my enterprise days. Productive if guided explicitly but if let run wild there's lots of WTF moments.
LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.
How many of those wtf moments are simply from not “being in the room when it happened?” Most enterprise software is riddled with wtf moments demanded as one compromise or another.
There's always wtf, why did we add this feature, but at least in my experience, once a week or so I run into something in this category. Me: "AI, please cleanup/refactor/improve this thing" AI: "Roger that! I deleted the file so now it's perfectly clean" ... insert W.T.F.
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.
"offshore Indian devs" are no slouches. They have access to the same GPT models and likely cost a tenth of the median US salary. Businesses are always looking to lower marginal cost. They will hire 1 software architect in US to write specs and 10 software developers in India to babysit 100 agents.
This is short-sighted. The problem with offshore Indian devs is the communication friction/overhead. You're 9 hours offset, with people who have decent-but-not-great English skills and wildly different cultural priors. If the product people/decision makers are in the US, you're getting a ~50% savings to suffer all those issues, while the cost of tokens remains unchanged. That 50% savings doesn't look very impressive when you're taking a 20% productivity hit from comms friction and crossed wire, and 35% of your total cost is from tokens anyhow. Then it comes out to be a very marginal savings, at the cost of a VASTLY worse hiring experience and VERY high variance of outcomes.
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.
My friend is an exec at a US software company and they are preparing to lay off a few teams of programmers in their Eastern European locations and replacing them with a small number of US programmers + AI. He said they are much more productive and produce new features much faster.
I think the article is right about outsourcing but not from cheap offshored contractors, good experts will become more independent and be more enabled to support more clients with AI, meaning small and medium businesses won’t need internal as many engineers, finance, marketing, etc
I have really been trying to get local models to work. I have tried different harnesses, tooling, skills, prompts, etc. But when I compare claude code with anthropic models or codex with gpt 5.5, vs qwen, glm or gemma and the same harnesses, the frontier models come out massively ahead. I am at the point where I just don't see the point of the non-frontier models, they waste more time than they save.
The hosted frontier models are massively subsidized, right? I think the point of local non-frontier models is just learning at this point, so you’ll be skilled if/when the market starts comparing the actual price of the two different models.
For agentic coding I 100% agree with you, it's worse and slower and more expensive for LARGE coding with local models. Narrow coding (like writing a specific function) is slow but viable. Regular LLM chat usage on high-end consumer hardware is competitive except on cost though. 0
>frontier models are more capable than the latest from DeepSeek. But is the capability difference enough to justify a 30x price difference?
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?
Free market competition? This is a pretty classic pattern. Leaders capture market with quality but run into trouble scaling, followers compete on price and availability. Given time, leaders eventually run out of upgrade runway and find themselves swallowed up by followers. Or alternatively, leaders think their lead is inevitable and miss a sea change or iterative upgrade path. Think IBM PCs before Compaq and other cheap clones ate their lunch.
I've been pretty happy sticking with codex 5.4 medium. I don't see a good case for switching to 5.5 at the cost of going through my token budget quicker.
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.
I used to be on 5.4 high for most of my work. I have switched completely to 5.5 medium now. I would highly recommend trying it out
- 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.
We trialed 5.5 and the same queries produced worse results. Not worth the cost increase. Even if there’s a token efficiency gain the higher cost wipes that out.
This is embarrassing but I find 5.4-mini on Low covers a substantial part of my and my colleagues work.
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.
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.
For sure true for specialized ones like MedGemma (healthcare). In my testing, the 27b model is at least on the same level as frontier, and in some cases outperforms them. 4B is insanely good too for some lighter workloads. Thanks G for working on this!
$1100/m for an outsourced engineer… am I missing something? That’s far too low. Even juniors in South America tend to ask for at least double that number before factoring in the DeepSeek cost.
I thought the same thing. The author's reference point for LCOL developer seems a bit outdated. With what we pay our teammates in Colombia, the model pushed out to 22 months before crossover.
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.
I think this is a compelling argument, but I think 2 issues:
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.
Fair points. I used to think that until some months ago but the latest generation of OSS models are surprisingly good. Plus maybe it is the way I work, but I find myself constantly overriding the decisions of frontier LLMs (because they start degenerating towards god objects and spaghettification) so most use I have gotten out of the AI agents is really their ability to code quickly and syntactically correctly.
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
I wish Deepseek could read images. I've been having good luck guiding it around on personal projects, but anything that needs to render to a screen really needs to be looked at to see bugs.
> It looks vaguely comparable on benchmarks, but it tends to be fragile and a lot of management overhead in reality.
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).
What about it? Energy production basically has to equal energy consumption in the medium term, so if the grandparent comment is correct, it is 2x per capita.
Dunno how trustworthy this source is, but it says ~35 MWh/person in China and 77 MWh/person in USA.
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?
"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.
> "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.
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.
Absolutely. We are in a phase of "free money" for AI. Just as with the dotcom bubble that leads to 1) lots of experimentation, and 2) lots of infrastructure buildout (which includes AI model training). Once the money dries up, some infrastructure (including models) will turn out to be profitable, most won't. And some experiments will turn out to be successful, most won't. Lots of useful things will come out of that, both the failed and the successful attempts. Just as the dotcom boom payed real dividends 5-10 years later and laid the groundwork for the world we have today
> lowest energy costs will likely be able to dictate market prices
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
Not my comment, but I’d venture to guess they’re referring to the likes of DeepSeek et al, who are/will be able to host their top-tier inference infra more efficiently
right now the most likely outcome is that they are going to host locally produced much more power hungry chips, and even if the lead on electricity production will stay, it will be eaten by inefficiency of the hardware.
Unlikely. We have a big lead in terms of general computing devices, but China can leapfrog us with ASICs. They might still lag in the training space for a while but in terms of serving inference, USA is absolutely COOKED at the low-mid end.
I should have expanded, but basically, the OSS models becoming more and more capable to solve all day to day SWE coding needs will take a cut from frontier labs revenue.
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.
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?
Training models is as much art as science at this point. There's no gap in scientific acumen at Chinese labs, but the US has more real world experience in the art of training large models, and the US has the capital allocation lead.
This sounds to me like the Bitcoin bros. Yes, the first-gen technology was very energy-heavy, but afterwards people (bitcoin maxis and people who held the bag) kept insisting that all new technology is “shitcoins” and that everyone should just buy bitcoin.
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
Oh, and neural networks doing a huge number of floating point operations per word is not energy-heavy?
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.
Bitcoin is pretty much explicitly designed to use as much electricity as the market will allow, without becoming any more useful. If you removed 99% of the miners from the current system, Bitcoin will still be exactly the same - it won't be any faster or slower, and the same number of transactions will flow through. The cost of electricity serves only as a lower bound on the expected value of a coin.
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.
Bitcoin is a good analog because the goal was to create durable trust. The energy utilization is just a means to an end of fairly distributing new tokens to members of the network. There are many other schemes they could use and have considered adopting. The energy use is not necessary, it’s sufficient.
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.
Historically the winners in software have a flywheel that turns faster with more users. Facebook the more of your friends on it the better the product was. Google tracked how long users were on pages to improve search.
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.
The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding _that_ new user costs ~$20k/year (assuming 100k server and 5 year depreciation).
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.
I’ve been on this issue for a while now, models are not going to matter as much in the future. Pure energy cost will be the determining factor in who is most successful. The US just cannot build cheap energy the way other China can and at the scale that China will build it. 10 years from now it will be seen as the single source of advantage
If the cost of software development falls so precipitously that energy costs are a driving factor, that implies so many other changes that I don't know how we can trust any analysis of what would happen.
What is expensive in nuclear energy? Reason it is not .ore of nuclear reactors is not the cost, it is regulation. Regulation can be changed (it also seem to already have, recently, IIRC)
> they may have inadvertently priced themselves out of the market.
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.
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 $25/1M output-tokens.
Unless you mean that releasing open weights models is the loss leader, in which case, you might be right but I hope you're wrong. We've seen some of this from Qwen at least - their latest model is closed only. I hope there's always someone willing to make this bet and release better and better open models.
> I hope there's always someone willing to make this bet and release better and better open models.
What would this bet be? Training is expensive and open weights mean that for hosting you compete on price with people that don't have this item on their bill.
When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing.
The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team people that lack motivation and foundational skills.
Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus and tiny distillations from DeepSeek that perform well only in benchmarks.
> When discussing LLM pricing, people are missing the plot. [ ... snipped ...] Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing.
And you think it is unreasonable to consider this unsustainable?
And the direction is definitely towards removing that subsidy really soon. We can see it with OpenAI's shift to API-equivalent pricing for enterprise customers last month. Anecdotally my company saw OpenAI credit usage grow 2x with stable use across the ChatGPT platform, which is pretty terrifying considering just 2% of the company uses Codex. (For context, ChatGPT business subscriptions give you a fixed pool of credits to use, after which you get billed a la cart at inflated 1.75x rates vs API, or get access to anything but the non-reasoning models turned off.)
Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be.
I learned today that the Anthropic "Enterprise" plan - the one big companies use because they need governance features and audit logs and all of that jazz - is billed at API token rates (plus $20/seat/month).
So large companies are getting billed a lot more than those discount subscription plans.
Isn't the plot that it's like an infinite bikeshed but 10% of the biksheds are actually trailer parks and when you finally realize it's a trailer park and not a bike shed you're down 10-100$ because it's token gen is faster than you can actually validate?
Some might say the price wouldn't be great if you could actually process and validate it...
I think this misses the forest for the trees. Working with ChatGPT is eerily similar to working with offshore Indian devs back in my enterprise days. Productive if guided explicitly but if let run wild there's lots of WTF moments.
LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.
Certainly tracks with the number of outsourced teams begging for work on LinkedIn.
How many of those wtf moments are simply from not “being in the room when it happened?” Most enterprise software is riddled with wtf moments demanded as one compromise or another.
There's always wtf, why did we add this feature, but at least in my experience, once a week or so I run into something in this category. Me: "AI, please cleanup/refactor/improve this thing" AI: "Roger that! I deleted the file so now it's perfectly clean" ... insert W.T.F.
Never seen that once.
At least some, but let me give an example.
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.
"offshore Indian devs" are no slouches. They have access to the same GPT models and likely cost a tenth of the median US salary. Businesses are always looking to lower marginal cost. They will hire 1 software architect in US to write specs and 10 software developers in India to babysit 100 agents.
This is short-sighted. The problem with offshore Indian devs is the communication friction/overhead. You're 9 hours offset, with people who have decent-but-not-great English skills and wildly different cultural priors. If the product people/decision makers are in the US, you're getting a ~50% savings to suffer all those issues, while the cost of tokens remains unchanged. That 50% savings doesn't look very impressive when you're taking a 20% productivity hit from comms friction and crossed wire, and 35% of your total cost is from tokens anyhow. Then it comes out to be a very marginal savings, at the cost of a VASTLY worse hiring experience and VERY high variance of outcomes.
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.
My friend is an exec at a US software company and they are preparing to lay off a few teams of programmers in their Eastern European locations and replacing them with a small number of US programmers + AI. He said they are much more productive and produce new features much faster.
I think the article is right about outsourcing but not from cheap offshored contractors, good experts will become more independent and be more enabled to support more clients with AI, meaning small and medium businesses won’t need internal as many engineers, finance, marketing, etc
I have really been trying to get local models to work. I have tried different harnesses, tooling, skills, prompts, etc. But when I compare claude code with anthropic models or codex with gpt 5.5, vs qwen, glm or gemma and the same harnesses, the frontier models come out massively ahead. I am at the point where I just don't see the point of the non-frontier models, they waste more time than they save.
The hosted frontier models are massively subsidized, right? I think the point of local non-frontier models is just learning at this point, so you’ll be skilled if/when the market starts comparing the actual price of the two different models.
For agentic coding I 100% agree with you, it's worse and slower and more expensive for LARGE coding with local models. Narrow coding (like writing a specific function) is slow but viable. Regular LLM chat usage on high-end consumer hardware is competitive except on cost though. 0
0 - https://www.williamangel.net/blog/2026/05/17/offline-llm-ene...
>frontier models are more capable than the latest from DeepSeek. But is the capability difference enough to justify a 30x price difference?
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?
Free market competition? This is a pretty classic pattern. Leaders capture market with quality but run into trouble scaling, followers compete on price and availability. Given time, leaders eventually run out of upgrade runway and find themselves swallowed up by followers. Or alternatively, leaders think their lead is inevitable and miss a sea change or iterative upgrade path. Think IBM PCs before Compaq and other cheap clones ate their lunch.
I've been pretty happy sticking with codex 5.4 medium. I don't see a good case for switching to 5.5 at the cost of going through my token budget quicker.
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.
I used to be on 5.4 high for most of my work. I have switched completely to 5.5 medium now. I would highly recommend trying it out
- 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.
We trialed 5.5 and the same queries produced worse results. Not worth the cost increase. Even if there’s a token efficiency gain the higher cost wipes that out.
This is embarrassing but I find 5.4-mini on Low covers a substantial part of my and my colleagues work.
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.
I disagree with every part of this.
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.
Local models will get improve. They will get smaller, faster, cheaper. This is already happening https://dev.to/bspann/bitnet-microsofts-1-bit-llms-that-run-...
AI will become a commodity technology the same way virtual machines are a commodity.
I don't see local AI taking off. Memory costs make it impractical. Deepseek API pricing is not a suitable analogue because it's not local.
For sure true for specialized ones like MedGemma (healthcare). In my testing, the 27b model is at least on the same level as frontier, and in some cases outperforms them. 4B is insanely good too for some lighter workloads. Thanks G for working on this!
$1100/m for an outsourced engineer… am I missing something? That’s far too low. Even juniors in South America tend to ask for at least double that number before factoring in the DeepSeek cost.
I thought the same thing. The author's reference point for LCOL developer seems a bit outdated. With what we pay our teammates in Colombia, the model pushed out to 22 months before crossover.
Premium services need to allow enterprises to self host the services to reduce cost of inference. Another advantage is data doesn't leave the VPNs.
Always has been. People pay for the (not so) marginal performance gains.
First fix your website navbar and hero on mobile that was broken, and it shows that you vibe coded a slop!!!
The dark mode version of the site makes the tables unreadable.
Agreed, but same data is listed right below the table.
Thanks for flagging, fixed
> But is the capability difference enough [..]
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.
This is bogus.
I think this is a compelling argument, but I think 2 issues:
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.
Fair points. I used to think that until some months ago but the latest generation of OSS models are surprisingly good. Plus maybe it is the way I work, but I find myself constantly overriding the decisions of frontier LLMs (because they start degenerating towards god objects and spaghettification) so most use I have gotten out of the AI agents is really their ability to code quickly and syntactically correctly.
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
I wish Deepseek could read images. I've been having good luck guiding it around on personal projects, but anything that needs to render to a screen really needs to be looked at to see bugs.
> It looks vaguely comparable on benchmarks, but it tends to be fragile and a lot of management overhead in reality.
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).
[0]: https://github.com/EratoLab/erato
Only if you don’t allow construction of local data centers
I can name one big country that won't disallow data centers.
US has over 10x the number of data centers as China; and produces 2x more energy per capita than China.
what about energy consumption per capita?
What about it? Energy production basically has to equal energy consumption in the medium term, so if the grandparent comment is correct, it is 2x per capita.
Dunno how trustworthy this source is, but it says ~35 MWh/person in China and 77 MWh/person in USA.
https://ourworldindata.org/grapher/per-capita-energy-use
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?
"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.
> "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.
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.
Absolutely. We are in a phase of "free money" for AI. Just as with the dotcom bubble that leads to 1) lots of experimentation, and 2) lots of infrastructure buildout (which includes AI model training). Once the money dries up, some infrastructure (including models) will turn out to be profitable, most won't. And some experiments will turn out to be successful, most won't. Lots of useful things will come out of that, both the failed and the successful attempts. Just as the dotcom boom payed real dividends 5-10 years later and laid the groundwork for the world we have today
> lowest energy costs will likely be able to dictate market prices
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
> the closed labs won't catch up anytime soon
Which closed labs won’t catch up to whom?
Not my comment, but I’d venture to guess they’re referring to the likes of DeepSeek et al, who are/will be able to host their top-tier inference infra more efficiently
right now the most likely outcome is that they are going to host locally produced much more power hungry chips, and even if the lead on electricity production will stay, it will be eaten by inefficiency of the hardware.
Unlikely. We have a big lead in terms of general computing devices, but China can leapfrog us with ASICs. They might still lag in the training space for a while but in terms of serving inference, USA is absolutely COOKED at the low-mid end.
I should have expanded, but basically, the OSS models becoming more and more capable to solve all day to day SWE coding needs will take a cut from frontier labs revenue.
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.
> China's electricity generation going parabolic
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?
Training models is as much art as science at this point. There's no gap in scientific acumen at Chinese labs, but the US has more real world experience in the art of training large models, and the US has the capital allocation lead.
This sounds to me like the Bitcoin bros. Yes, the first-gen technology was very energy-heavy, but afterwards people (bitcoin maxis and people who held the bag) kept insisting that all new technology is “shitcoins” and that everyone should just buy bitcoin.
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
Bitcoin is a poor analogue for much anything since it's very much designed to be energy-heavy.
Oh, and neural networks doing a huge number of floating point operations per word is not energy-heavy?
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.
Bitcoin is pretty much explicitly designed to use as much electricity as the market will allow, without becoming any more useful. If you removed 99% of the miners from the current system, Bitcoin will still be exactly the same - it won't be any faster or slower, and the same number of transactions will flow through. The cost of electricity serves only as a lower bound on the expected value of a coin.
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.
Bitcoin is a good analog because the goal was to create durable trust. The energy utilization is just a means to an end of fairly distributing new tokens to members of the network. There are many other schemes they could use and have considered adopting. The energy use is not necessary, it’s sufficient.
Energy costs and privacy.
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.
Historically the winners in software have a flywheel that turns faster with more users. Facebook the more of your friends on it the better the product was. Google tracked how long users were on pages to improve search.
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.
>They're not going to lose on price. No consumer software ever has
Lists examples of software that are free to the users
Go on...
httpd
The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding _that_ new user costs ~$20k/year (assuming 100k server and 5 year depreciation).
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.
I’ve been on this issue for a while now, models are not going to matter as much in the future. Pure energy cost will be the determining factor in who is most successful. The US just cannot build cheap energy the way other China can and at the scale that China will build it. 10 years from now it will be seen as the single source of advantage
You mean coal?
If the cost of software development falls so precipitously that energy costs are a driving factor, that implies so many other changes that I don't know how we can trust any analysis of what would happen.
> The US just cannot build cheap energy
Nuclear power anyone?
Cheap.
What is expensive in nuclear energy? Reason it is not .ore of nuclear reactors is not the cost, it is regulation. Regulation can be changed (it also seem to already have, recently, IIRC)
Same as bitcoin then.
> they may have inadvertently priced themselves out of the market.
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.
Yes and no. Just take a look at the OpenRouter providers page:
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.
> I hope there's always someone willing to make this bet and release better and better open models.
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.