[zozbot234]

People can't seem to agree on what "Opus class" even means (the latest Opus is apparently pretty weak) but DeepSeek Pro, Kimi and GLM all are quite capable.

[computerex]

Nothing compares to Opus when it comes to "taste" in web design in my experience. Nothing compares to opus in very difficult HPC/model inference development. I worked on this with opus: https://github.com/computerex/dlgo

OpenAI was offering 2x usage at one point and I still used opus just because it's so much more effective.

[lambda]

Which Opus?

Anthropic has been releasing models named Opus since 2024 with Claude 3 Opus.

Opus has gotten vastly more capable since then.

Local model far surpass Opus 3. They even surpass Opus 4 on most benchmarks.

Sure, if you compare to the latest Opus 4.8 or even 4.6, they're not there yet. But there's a huge difference in performance between 4 and 4.8.

[jkells]

Can't speak for anyone else but there was a step change in frontier models last November. Opus 4.5 and GPT 5.2 I think.

When I colloquially say Opus level I really mean Opus 4.5 or later

[lambda]

Right. Local models haven't quite hit that level yet. The biggest open models, which you need tens of thousands of dollars of hardware to run at reasonable speed, have pretty much hit that level of capability, but most models you can reasonably run at home aren't quite there yet. But given the gap, if local models keep improving, you'd expect to maybe see that level by this November.

[zozbot234]

My understanding is that we could in fact run the largest models on "reasonable" home hardware by focusing on throughput rather than raw speed and having them do unattended inference in large batches. The big proprietary suppliers have no interest in this because their own incentive is to fill all the physical space available with top-performing hardware and doing huge amounts of inference as quickly as possible. A home user with limited hardware investment has very different constraints.