alt Hacker NewsAstra and 5.5 came out in september, so I didn't compare to them I compared to "where the SOTA was less than a year ago".
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.
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It’s a $20-100 a month outlay for an otherwise low expense discipline to access the best models available, without any local dev ops requirement or expense. If access ever becomes a problem, the open models are there. That’s a fairly straightforward calculation for a lot of people.
This is a great example of why benchmark racing is useless for LLMs.
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.
It doesn't matter because a lot of us won't pay for Astra or Fable.
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 can ask it to do something and it does it.
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".
I don't need or want that
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).
Local models at that scale just aren’t comparable to the frontier. I’m a big DeepSeek glazer but even the gap between full DS 1017 and Opus 5.5 is vast. Local models will never be able to reach that level, there’s real limitations at play. SOTA models are getting bigger and bigger.