alt Hacker NewsI'm arguing we can't trust retail prices because the marginal pricing isn't meaningfully connected to it anyway.
But if we have to look at what we think margins might look like, DeepSeek continues to host v4 Flash at the existing price despite competitors beating it in price (https://openrouter.ai/deepseek/deepseek-v4-flash), so there's at least one example of a Chinese lab charging a predetermined price despite competition. And no one but Moonshot is hosting Kimi K3 yet (https://openrouter.ai/moonshotai/kimi-k3). Perhaps there's room in the market for those who release their models to make margin on them.
And I believe my Composer example speaks for itself. The open models are behind but there's tangible proof they can be tuned for pareto frontier efficiency. See "Cost per Task" at https://artificialanalysis.ai/agents/coding-agents.
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Deepseek is still charging cached input at 1/10th the price of any competitor.
For an example of my real token usage for a day with DS: Input (Cache hit) 530,949,760, Input (Cache miss) 7,875,004, Output 1,389,685 - it is still 1/5th the price of Baidu (the cheapest) and 1/7th-1/10th the price of US hosts.
Also, Deepseek platform is not the same thing as Deepseek open weights. There is a major misconception that the existence of an open weights model means that it is the same thing as the proprietary platform offering, but that is definitely not the case.
>DeepSeek continues to host v4 Flash at the existing price despite competitors beating it in price (https://openrouter.ai/deepseek/deepseek-v4-flash),
their competitors are discounted at around 33%, so it's safe to say that's the margin, maybe less if their competitors have worse caching or quantization. Meanwhile claude code/codex resellers selling tokens for 90% off API price, presumably by reselling usage from fixed consumption plans, which gives an idea on how fat the american labs' margins are.
>And I believe my Composer example speaks for itself. The open models are behind but there's tangible proof they can be tuned for pareto frontier efficiency. See "Cost per Task" at https://artificialanalysis.ai/agents/coding-agents.
But composer is a closed model? If it's really that easy to get better coding performance, why haven't the chinese labs replicated it? And this is all assuming the performance boost is real and not from benchmaxxing. Moreover if you apply the "street price" discount I mentioned above, American labs look far more favorable.