So even for enterprise deployments, as the dust settles down, CFO/CTOs might find out that deploying on an internal cluster of GPUs is far more cheaper and reliable for their organisational needs than paying someone else for burned tokens.
And I was dead wrong. Now I mostly use DeepSeek Pro myself.
I actually think that's still true and will continue to be true as long as someone else subsidizes the tokens. Once the "free money" runs out, things will get interesting.
The most I’ve ever spent in a month extra on API tokens for my own work is $200, and I pay for the $200/mo Claude. I use these models quite a lot, though not idly (I usually just walk around and do other stuff until I know how im going to approach the next set of problems). So it costs me about $3000/year to get as much as I want of the best model available. Already that seems low enough to not be worth stressing out too much about optimizing it, because it feels like an indisputable good value, and trying to save money with a less powerful model would be optimizing for a $1000-$2000 saving at the expense of a large portion of my work taking longer or being more frustrating and iterative.
That’s not a flex or anything, I get that in other countries $3000/yr is a lot of money for a software developer and also a lot of people would perhaps rationally be better off doing X% worse at work or spending Y% more time on tasks to save $Z, if their productivity improvements didn’t translate to more salary. Otherwise if your performance has more upside I really do think that the smartest models are better with the current pricing scheme. Deepseek and the other Chinese models spend a LOT of time thinking, and tend to be much more jagged (benchmaxxed) in performance. How can dealing with that over an entire year be worth $2k?
The only situation I can think of where sacrificing my own time/performance to save on inference is batch compute (of course, $1k vs $100k is different from $30 vs $3k) or work where the tier 2 models have crossed the “good enough” threshold. But I think Opus is not even close to that threshold generally yet. As it gets smarter I, and I think most others probably, just try to do harder things faster and hit the next wall.
1. The sheer number of tokens that a coding agent can use flipped the math upside down on this equation. If you use the most expensive model for everything those costs quickly become untenable, even for software companies.
2. We realized many of the coding problems we're solving aren't incredibly difficult.
I've wasted over a hundred Euros re-doing work that was done badly due to the model not being up to task (Vue with TS + wrapper components around PrimeVue, needing to handle event and property passthrough and deal with the stupid Vue SFC issues, TS made this much worse than JS would be). I think it was the GLM model through Cerebras Code at the time, in addition to some GPT and Gemini models with the API pricing.
That said, DeepSeek V4 Pro is pretty good and I can totally see myself offloading some of the work, as long as a better model reviews the work and provides suggestions/tests for it.
A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.
doesn't invalidate the rest of us working on tough problems that demand more expensive models and valuable enough to justify it
Its just that some of us didn't imagine having GPUs would be advantageous and were not gamers on the side. Those who had beefy GPUs or GPU rigs for any reason, they rarely need to go anywhere else.
At least I am so impressed with Deepseekv4 AFTER using Claude Opus 4.7 for significant amount of time that I am not going anywhere but Deepseekv4.
The model is just INSANE. Things I have done with it include attempting to write a 2.5D game engine in C with full animation and map rendering layer by layer.
I don't think it's as simple as saying China's hosting is subsidized, they have generally cheaper electricity and labor costs than in the US and don't have access to the top tier models, and a large internal market where the big models are the best thing they can run with what they have. So obviously they max out on their top models (which are trained with their hardware market in mind, not ours) and get the economy of scale from that, and can run generally the same hardware for less money than in the US because
The edge models are very cheap to run and can do so on inexpensive hardware. They are like 95% cheaper to run than Haiku, so the math is in their favor for certain batch workloads. Most people just run the models for themselves when they do that without making it available on openrouter or whatever, because you can just provision a gpu node and use it as needed, and it's not that expensive to run this family of models.
Is your problem that you want to call Chinese models hosted in the US because you're worried about the data handling?
If you want to support a team of engineers, DeepSeek V4 Flash is antirez's current favorite. And you could support a team of engineers pretty nicely for $40-50k. Which might not make sense if you're on a Claude MAX 5x plan or the old enterprise group plan with fixed price seats. But Anthropic is switching their enterprise contracts over to token-based pricing, at which point $50k is looking pretty good.
Not nearly as cheap as the Chinese infra but still pretty cheap.
I just used ollama with a shell script to tackle my directory of papers/literature. I converted the first 6 pages of each document to PNG, handed them off to Qwen, and told it to spit out BibTeX, including the abstract. Two days later it was done, and I didn't spend anything on "tokens."
Chinese models are really quite good at a lot of stuff.
I think you're right especially if you're someplace that already has a data center, such as a university. Solves a lot of privacy concerns as well.
Just like they did with the US steel industry in the 80s.
I don't see myself returning to Claude or Codex anytime soon.
