I had been saying this on HN repeatedly: people are going to use the smartest models for coding. They don't care how cheap your tokens are if they don't have the highest probability of solving your programming tasks.

And I was dead wrong. Now I mostly use DeepSeek Pro myself.

> And I was dead wrong. Now I mostly use DeepSeek Pro myself.

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.

I pretty strongly feel the opposite way. Granted I have not used deepseek enough to “know” their model idiosyncrasies as well as Anthropic, so there is a partial skill issue. But I just find it really hard to justify using a less powerful model while I work.

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.

Your comment is a slice of the reasoning underlying the "AI will take all the jobs" claim. I would constantly see references to what AI could do and how fast it was improving. Never a word about cost. We should anticipate that there will always be demand for human labor, for cheap models, for local models, and probably even frontier models.

Yeah I've also found that models are good enough that the extra spend on premium models isn't always worth it, particularly for my small personal toy projects.

A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.

The other thing that's changing is more and more CFOs are looking at the AI spend in engineering departments and hitting the brakes. Token leaderboards were cool when the spend wasn't a double-digit-percent of the entire department's budget including salaries.

I mean indsight is 20/20, but saying that is like saying "everyone will just use the best tools". That's not what we see most places in the world for most types of resources.

I think two things happened:

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.