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.

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.

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?

Developing these things is NOT free, there's a lot of labor, hardware, compute/memory/storage/network that goes into that. Who's paying for all this? Chinese govt? Developers themselves? What's the revenue model here?

I absolutely LOVE ability to either run them locally or access inference providers on the cheap, but having a hard time understanding the financial side of this.

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.

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.

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.

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.

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.

Most of the cost of supplying inference compute is depreciation of the GPUs. Maybe Deepseek is anticipating a 50 year life for theirs.

anyone who disagrees is not seeing the forest, only the trees.