Comparing the sort of models that would run on that to frontier models like Opus 5.5 or Astra is silly
But ok, let's compare: https://benchlm.ai/compare/gpt-6-astra-vs-qwen3-8-flash-next
Astra's score: 85.8/100
Qwen3.8-flash-next's score: 55.87/100
So there's a ~30 pt difference between them. Let's add 5% more for quantization damage.
My perspective is, yes - that's a huge difference. No - it doesn't matter. Flash-next is above the threshold of useful intelligence. I can ask it to do something and it does it. Sure, it burns some tokens on bad ideas on the way, and sure it can't turn a single paragraph prompt into million dollar business. I don't need or want that - I want something I can ask to refactor a module, and it does it and it doesn't stop until the module compiles and all the tests pass. flash-next does that.
I run my own local models, and I can tell that yes, one ask and they do... with many more errors, and much much slower, compared to the lowest levels of frontier models.
Sure, if one has no standards at all, local LLMs definitely are "above the threshold of useful intelligence".
Putting aside the absurd straw man, you’re touching on an important point: these small open weight models are sufficient for how you like to work, but it’s not remotely good enough for how I like to work. Opus 5.5 is barely sufficient for how I like to work (many parallel work streams, as little of me in the loop as possible, etc).
So not really work, then?
You’re automating yourself out of a job using capital you don’t own, and you think your position will hold? I am skeptical.
And I work for myself, so no, not automating myself out of a job.
Although I suppose I’m automating the people I won’t hire now out of a future potential job.
I work like the parent poster does. I am working harder than before the AI tools came around. I am just working at a higher level. Not too dissimilar from someone moving from programming in assembly language to significantly higher level language.
> as little of me in the loop as possible
You may be confused about this. If I am running just one loop at a time, you'd be right. What modern tools allow me to do is think more like a supervisor that has dozens of engineering teams available to him. I always have a long list of problems I need to solve. I used to sit with one and bang on it until it would pass acceptance criteria. Sometimes that took months. Now I just farm out each task to an agent orchestrator, provide it details on what I want done, a bit of design and architecture and acceptance criteria. I then step away from it and move to the next agent orchestrator and repeat the process for the next task. I usually have 6 or 7 large tasks being worked on at the same time. This is currently my personal limit. I can't babysit more than that. But as the models and harnesses get better I suspect I will have to do less babysitting and this process will get more autonomous.
It may sound like this process would remove the need for engineers. In my organization it is doing quite the opposite. As the organization figures out that we can do a lot more, we change our plans to build more. A lot more. Building still requires engineers, more than before, for us. But only those who can think like architects and can manage large teams of AI agents and orchestrators that work 24x7.
Don't have to be. They just have to be good enough for the tasks you need.
If you’re using open weight models to code basically the same as you always have been but just a little faster, while your competitors are using frontier models to go 10x faster, you haven’t saved anything.
Now, granted, the real question comes down to whether they’re going 10x faster, and whether that’ll hold up in the long term. But that’s the gamble as I see it. And to me, open weight models aren’t worth the risk. $200 is 0.5% of my little company’s revenue. It would be foolish of me to try and save that money.
For how long? Give it another year, and they will be.
You can rent your very shiny version of the tools you use for a very reasonable price, but to me the bent and rust-stained tools I own myself are worth much more.
I use both models extensively and there is no way I would ever agree that Astra and Qwen3.8-FN are that close in the real world.
I agree with the other comments: These comparisons are silly.
You keep saying that you don’t understand why anyone would have a Claude subscription and reciting ideological reasons not to. I’m assuming this means you don’t use their new models much? That’s the only explanation I can think of for how someone could think that Opus 5.5 or even Sonnet 5.5 and Qwen3.8-FN are close.
I use them both (often Qwen3.8-FN as a subagent) and any claims that they’re actually pretty close read as downright delusional to me.
QFN comes in above Sonnet on some benches I've seen. And let's not forget that Qwen 4 Flash is due any day now.
I'd rather wait all day for an impeccable implementation of a single feature than get 10 features worth of "okayish" code
For example, suppose you have a list of questions in a CSV, and you just want to categorize them by the "type" of question (e.g. technical, bureaucratic, procedural, a mixture, etc). You could have Fable do it, and it will work, but it probably won't work significantly better than Haiku, or even one of the cheaper Qwen or Deepseek models.
A lot of my AI usage does boil down to that. I use Opus for most of the actual tasks involving writing code, but for more mechanical and less complicated stuff I prefer the speed and the lower usage of my tokens and defer to Sonnet or Haiku.