It would not be surprising if GPT and Claude get cheaper too as inference gets cheaper. Two years ago, o1 was the strongest model and cost much more than Fable, while being nowhere near as smart as a Qwen 3.6 35B that you can now run on a DGX Spark without much trouble.
> It would not be surprising if GPT and Claude get cheaper too as inference gets cheaper
No because the biggest factor in their current price is VC subsidization which has likely peaked if OpenAI is now serving ads and Anthropic has increased their API pricing
This is what concerns me about how AI giants are planning to make money. Their product has already been commoditized at prices which for them are still subsidized to grab market share. Unless the giants invent a technological leap, their prices are going to be dragged down by open weight models and I don't see how they'll turn a profit.
With cache hit rates being effectively free, harnesses like Reasonix have let me do a month of work for less than 2 dollars. It's not even the subsidies making it cheap, American providers like Digital Ocean or Cloudflare host the same model with similar pricing.
The giants knew this was coming, and soon 95% of AI tasks will be able to be done by open models (coding, research, cowork style work). So why pay a premium? Why use them at all? This leaves the labs with two options:
1) push the frontier in a way only massive scale can, and cash in on it (mythos level cyber security, recursive training, frontier science work). There’s big money for never before possible capabilities.
2) own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
Will it be enough to justify a Google level valuation? We’ll see how fast they can push it.
Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.
3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.
#1 isn't going to happen because we're actually data limited, not compute limited. You can throw all the compute in the world at bad data and it won't make a difference, but an undertrained model with perfect training data will absolutely slay.
#2 isn't going to happen, because these labs have shown they have limited app/design sense, and they also lack the industry connections and domain wisdom to execute.
The way things are actually going to go is that these labs will set up partnerships with huge biotech/engineering/etc firms, and do custom training/inference on specific tasks that promise to be wildly profitable with them, then take royalties on the creation in perpetuity. Why sell inference when you can partner with Pfizer to make a version of Ozempic that also makes people freaky jacked, or partner with Bectel to make a radically safer, more efficient Nuclear power plant?
Let's imagine that Anthropic/OpenAI fail to manufacture scarcity by villainizing Open Weight models (a sincere probability). What is left for these corporations to prop up their prices, or any margin at all? I expect scaffolding around tool use, supporting bespoke implementation and driving risk down for institutional adoption. (They might even build an insurance tool to protect accountants/lawyers from errors in compounded probabilism!)
A question for economists... It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
> It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
Isn't this the thing people have said about every new technology since the printing press? And it has been mostly true, but it has also been the case that the incumbents have fought hard to lock things back up again. Newspapers and radio stations buy each other up, the open web gets locked inside Facebook (which, 30 years ago, people were already worried about with AOL), people have computers in their pockets they can't run their own programs on anymore.
Interests are going to want to lock the new information thing behind a gate so they can charge a toll and censor what they don't like, same as it ever was. You don't win by default, you have to fight to stop them.
One thing it doesn't even mention is how good those models are. Evet since I moved to DeepSeek I had zero regrets. It performs exceptionally well. I honestly prefer it to ChatGPT (or Claude that I use at work).
I never used Fable, maybe it is that much better. DeepSeek has no problems with the workloads I give it though - if it only keeps marginally improving with each interaction I don't see myself needing to come back.
Open weight and local hosting is far, far cheaper. In every respect. Even support is cheaper, over time.
However, it's difficult to sell this to businesses who want contracts and KPIs, not staff and commitments.
Regulated industries will favour the closed sources, either by choice or mandate. The interesting question is whether they will have better models, or worse models. History says they will receive a worse service, but continue anyway.
The token-economics for closed source models are different, they are optimizing for 200 USD tokens worth of software engineer monthly usage, they will increase per token price as models or harnesses are more optimized.
Aren't these open models so cheap because they're (partially) chinese gov. sponsored, and because they're stealing and redistributing the IP that comes in?
Whose IP do you think they are stealing? According to US courts, training is fair use. And even if it wasn't, they are distilling output from other models, which isn't copyrightable, again according to US courts.
One of the purposes of open weight models is to create a moat. If there were no open models available, I think we'd see much more and better models coming from Europe by now. Right now, any startup wanting to build and sell a model needs to be substantially better than the open models, which has become increasingly difficult and expensive.
i agree with his statement that the big companies and the string pullers in government are inching toward banning open models.allowing the plebs unrestricted access to things seems against the wishes of the "you will own nothing and be happy" / "you will rent everything on the cloud and subscribe to your appliances" crowd such as blackrock and so on.
anyone who disagrees is not seeing the forest, only the trees.
I don't see how they could ban them in the US. Code is speech, and the first amendment still mostly holds. They might try, but I don't see the courts upholding it.
90% of my model use is on local open-weights models.
The things that I need to automate do not need frontier models. Heck, even a gemma-4-12B-it-qat-UD-Q4_K_XL can deal with a lot of complexity if properly guided (it can run on 16GB of unified memory, for example on a base model Macbook Air).
I've been using it to translate Javascript to a custom scripting language in a product I work for, just by providing a system prompt and an MCP tool to call the target compiler to check for errors.
Sometimes it converges faster than Opus 4.6 (I've tried) because it doesn't over-think stuff.
If it were a person I would say it knows less, but it's still smart.
I mean, you don't need the most powerful tool at all times.
We treat AI as one-size-fits-all, and once cost gets in the way, it will matter.
I don’t get it. So many here are saying open weight models will kill the frontier labs. But open source and similar have tried to beat private companies everywhere all the time, and people still buy the best products even if great open source alternatives are available.
Why wouldn’t this be the case for AI too?
Even if open weight models were vastly more expensive, I would still prefer them. I don't know where my data is going and whether they're lying about the model when I make an API call. They can ban you from their API for any reason. Anthropic recently pulled their frontier models. There are numerous compliance concerns. The list goes on and on.
Aside from googling "how to download and run open weights model" check out localllama (yes 3Ls) subreddit. Huggingface.co is where many of them are published.
There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).
The government is going to ban foreign models and foreign inference providers, without question. The US govt is going to dig its dirty little fingers into OAI/Anthropic/Oracle/(probably)SpaceX and end up taking some stock for a sovereign wealth fund (probably timed to prop up flagging share prices, and with the promise of sweet government grift down the line), and at that point the bans will be framed as protecting that investment.
One issue I keep seeing with cost comparisons is that they compare API rates while a substantial fraction of users are on subscription plans.
It's more expensive to use GLM 5.2 paying z.ai or Opencode Zen API rates than it is to use Opus on a subscription plan. Both of those providers offer subscriptions priced favorably relative to their API rates, but only in what are effectively trial sizes.
> What worries me about this is that Anthropic and OpenAI seem to have backed themselves into a corner of high costs. Can they reasonably decrease their prices by 20-50x to compete with DeepSeek or Xiaomi’s Mimo?
They have high prices, not high costs. They will obviously keep prices as high as they can for as long as they can, while keeping demand up. Once demand starts to fall, so will the prices.
> Are these models cheap because they are open weight and having hundreds or people stress test running them on different hardware helped to lower the cost? Or is it that they are being provided as loss leaders to drive the prices down?
Neither. They are cheap because they have neither technical edge nor brand power to keep the prices high, and so have to ask commodity prices for them.
People somehow still don't get it, despite everyone who studies the economics of it telling them: Inference is dirt cheap. Training is expensive, inference is cheap, and getting cheaper.
So they reduce their prices 50x and their revenue drops to $1B a year? And they're supposed to be worth $1T each? If there is an open model offering at the cost of inference + whatever margin you need to stay alive, then as inference falls Anthropic/OpenAI revenue also falls.
What's the end-game? It sounds like in this business, if you are right, your revenues drop every year.
I'd appreciate an explanation of what "open weight model" means. Is it a "weight model" that is open, or a model with open weights (so should be "open-weight model"), or is it weights that can be applied to a model?
Are weights separable from a model? And if not, what is the point of saying "open-weight model" instead of just "open model?"
To the newcomer, it's hard to determine what the components of an AI system are from the throwing-around of these terms.
As another commenter said a "model" is a file (or group of files, there's multiple formats available; GGUF format is all in one file for example). You download it to the hardware of your choice (ie your own desktop with NVIDIA GPU). You run the inference engine (llama-cpp, ollama,lm studio etc) and tell it where the downloaded model is and it runs inference (so you can start chatting with it, or run agents).
"Open weights model" means the developer made the model available for everyone for free. You can download it from huggingface.co for example and do whatever you want with it.
Why "open weights" and not "open source"? Because the "source code" for LLM would include things like training data, training methodologies and tools, so that you can do the training and produce the model (files) yourself. That would be like compiling from source code. Which is not done with these models, it's company's know-how, they only share the end result.
It's more analogous to "freeware" which is what we traditionally call freely distributed binary executable files. But people started calling them "open weights" instead and the term stuck.
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[ 1.4 ms ] story [ 68.7 ms ] threadNo because the biggest factor in their current price is VC subsidization which has likely peaked if OpenAI is now serving ads and Anthropic has increased their API pricing
1) push the frontier in a way only massive scale can, and cash in on it (mythos level cyber security, recursive training, frontier science work). There’s big money for never before possible capabilities.
2) own the app layer with their edge in reputation and powered by their infrastructure. Be apple where everyone else is Linux. Do design, coding, research, SMBs, legal, finance, healthcare and more (they are doing all of this).
Will it be enough to justify a Google level valuation? We’ll see how fast they can push it.
Apple and Linux barely even compete in the same markets. Linux runs on the servers and embedded devices, Apple on the smartphones. Android is technically Linux but not in the "is a good analogy for open weight models" sense because Android is so deeply under the thumb of Google. The main place Linux and Apple actually compete is for PCs and laptops, and that's the market where the thing with 65% market share is Microsoft.
3. Try to get the government to "certify models" to cause regulatory capture which is what both Anthropic and OpenAI has been pushing. No certification no use in business.
#2 isn't going to happen, because these labs have shown they have limited app/design sense, and they also lack the industry connections and domain wisdom to execute.
The way things are actually going to go is that these labs will set up partnerships with huge biotech/engineering/etc firms, and do custom training/inference on specific tasks that promise to be wildly profitable with them, then take royalties on the creation in perpetuity. Why sell inference when you can partner with Pfizer to make a version of Ozempic that also makes people freaky jacked, or partner with Bectel to make a radically safer, more efficient Nuclear power plant?
A question for economists... It seems plainly clear to me that information and information processing is commodifying (for the first time in human history?). Without the age-old bottlenecks at the top of the value chain, capital will surely flow downwards, right?
Isn't this the thing people have said about every new technology since the printing press? And it has been mostly true, but it has also been the case that the incumbents have fought hard to lock things back up again. Newspapers and radio stations buy each other up, the open web gets locked inside Facebook (which, 30 years ago, people were already worried about with AOL), people have computers in their pockets they can't run their own programs on anymore.
Interests are going to want to lock the new information thing behind a gate so they can charge a toll and censor what they don't like, same as it ever was. You don't win by default, you have to fight to stop them.
I never used Fable, maybe it is that much better. DeepSeek has no problems with the workloads I give it though - if it only keeps marginally improving with each interaction I don't see myself needing to come back.
However, it's difficult to sell this to businesses who want contracts and KPIs, not staff and commitments.
Regulated industries will favour the closed sources, either by choice or mandate. The interesting question is whether they will have better models, or worse models. History says they will receive a worse service, but continue anyway.
Until your country will appear on naughty list of US administration because your local politician did something what mildly inconvenienced US oligarch
anyone who disagrees is not seeing the forest, only the trees.
The things that I need to automate do not need frontier models. Heck, even a gemma-4-12B-it-qat-UD-Q4_K_XL can deal with a lot of complexity if properly guided (it can run on 16GB of unified memory, for example on a base model Macbook Air).
I've been using it to translate Javascript to a custom scripting language in a product I work for, just by providing a system prompt and an MCP tool to call the target compiler to check for errors.
Sometimes it converges faster than Opus 4.6 (I've tried) because it doesn't over-think stuff.
If it were a person I would say it knows less, but it's still smart.
I mean, you don't need the most powerful tool at all times. We treat AI as one-size-fits-all, and once cost gets in the way, it will matter.
harness <-> gateway <-> inference provider
Easy to switch any of them and (mostly) possible to combine any with any
Replacing something like Excel is crazy-hard because of network effects, replacing an Enterprise CRM is akin to a removing a metastasizing cancer
There's many providers that run open weights models and give you access. Many decent open weights models cannot be run on consumer-grade hardware (DeepSeek, GLM, many others).
It's more expensive to use GLM 5.2 paying z.ai or Opencode Zen API rates than it is to use Opus on a subscription plan. Both of those providers offer subscriptions priced favorably relative to their API rates, but only in what are effectively trial sizes.
Most of the cost of supplying inference compute is depreciation of the GPUs. Maybe Deepseek is anticipating a 50 year life for theirs.
They have high prices, not high costs. They will obviously keep prices as high as they can for as long as they can, while keeping demand up. Once demand starts to fall, so will the prices.
> Are these models cheap because they are open weight and having hundreds or people stress test running them on different hardware helped to lower the cost? Or is it that they are being provided as loss leaders to drive the prices down?
Neither. They are cheap because they have neither technical edge nor brand power to keep the prices high, and so have to ask commodity prices for them.
People somehow still don't get it, despite everyone who studies the economics of it telling them: Inference is dirt cheap. Training is expensive, inference is cheap, and getting cheaper.
What's the end-game? It sounds like in this business, if you are right, your revenues drop every year.
Are weights separable from a model? And if not, what is the point of saying "open-weight model" instead of just "open model?"
To the newcomer, it's hard to determine what the components of an AI system are from the throwing-around of these terms.
"Open weights model" means the developer made the model available for everyone for free. You can download it from huggingface.co for example and do whatever you want with it.
Why "open weights" and not "open source"? Because the "source code" for LLM would include things like training data, training methodologies and tools, so that you can do the training and produce the model (files) yourself. That would be like compiling from source code. Which is not done with these models, it's company's know-how, they only share the end result.
It's more analogous to "freeware" which is what we traditionally call freely distributed binary executable files. But people started calling them "open weights" instead and the term stuck.