alt Hacker News"The final point I need to make, which does flatter the closed labs, is that the likes of Anthropic and OpenAI moved on from caring about these indices months, or even years, ago. Their focus is to a large majority on supporting internal uses – like AI R&D automation – and the use-cases of customers who are spending billions of dollars."
Is this true? If yes it makes a lot of sense. Time and time again, when I try some newly released open weights model I am disappointed. Even some older Claude models are beating them on quality of answers, even though they score less on intelligence in benchmarks.
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I believe so, this checks out with my experience, especially when comparing between the US models and Chinese models which benchmark scores are neck to neck with US ones.
My experience with Chinese open models is that they couldn't cope at all with my production work; both GLM 5.3 and Deepseek v4 ( or was it 4.1? Couldn't remember too well) went into infinite loop and wasted my tokens until my OpenRouter wallet reached 0; good thing I didn't enable the auto topup. US models, by contrast, breezed past them.
Even for simpler tasks, Chinese models took long time to complete, and I needed to supervise closely. The price for Chinese models, in the end, didn't come cheap, mainly because too much time was wasted on thinking, and this was regardless of whether they finished the tasks or not.
If they don't care, why are them presenting them when launching a new model, every single time?
> Is this true? If yes it makes a lot of sense. Time and time again, when I try some newly released open weights model I am disappointed. Even some older Claude models are beating them on quality of answers, even though they score less on intelligence in benchmarks.
Interesting. My experience is that Opus 5.5 is smarter than a model like Qwen3.8 Flash Next. But given the choice, I'd actually rather work with a model like Qwen3.8 Flash Next.
Even on medium reasoning, modern Qwen and DeepSeek models are ridiculously diligent. If I ask them a question about which library is best for a given task, they'll download 4 different candidates, read the source, and write test programs. Meanwhile Opus will look at a couple of web pages and give me glib bullshit answer. Opus is often like a brilliant coworker who can't be bothered to do their job. The modern Qwen and DeepSeek models are like someone who is merely smart, but who has an alarmingly good work ethic.
And I'm not just writing simple React CRUD GUIs. I'm doing stuff like modernizing ancient software forks, working on high throughput distributed systems, or finding bugs in other people's parser generators. And even the 120-240B open weight models are normally entirely adequate for these tasks.
One of the benchmarks where open models tend to perform badly is Terminal-Bench 4.0. I looked at some of the sample tasks on that benchmark, and some of they are really hard and specialized. Look, all I actually need out of a model is things like "tell me where this distributed algorithm might break", "get this ancient C code to build on modern Debian with modern dependencies", or "write a plan to migrate this database from MySQL to PostgreSQL and to test that the app still works." The modern open models can do all that, no problem.