> They still suck at explaining which model they serve is which, though.

"they" in this sentence probably applies to all "AI" companies.

Even the naming/versioning of OpenAI models is ridiculous, and then you can never find out which is actually better for your needs. Every AI company writes several paragraphs of fluffy text with lots of hand waving, saying how this model is better for complex tasks while this other one is better for difficult tasks.

Both Deepseek and Claude are exceptions. Simple versions and Sonnet is overall worse but faster than Opus for the same version.

Eh i mean often innovation is made just by letting a lot of fragmented, small teams of cracked nerds trying out stuff. It's way too early in the game. I mean, qwens release statements have anime etc. IBM, Bell, Google, Dell, many did it similarly, letting small focused teams having many attempts at cracking the same problem. All modern quant firms are doing basically the same as well. Anthropic is actually an exception, more like Apple.

it's sometimes not really a matter of which one is better but which one fits best.

For example many have switched to qwen3 models but some still vastly prefer the reasoning and output of QwQ (a qwen2.5 model).

And the difference between them: those with "plus" are closed weight, you can only access them through their api. The others are open-weight, so if they fit your use case, and if ever the want or need arise, you can download them, use them, even fine-tune them locally, even if qwen don't offer access to them any more.

If the naming is so clear to you, then why don't you explain: for a user who wants to use Qwen3-VL through an API, which one has better performance? Qwen3-VL Plus or Qwen3-VL 235b?

My precedent post should have answered this question. But since it didn't, I think I'm ill equipped to answer you in a satisfactory fashion, I would just be repeating myself.

Exactly. You're ill equipped to answer the question because you don't know. Qwen is terrible at explaining what the difference is, between the models that they serve on their API.

It's such a simple question: "For someone who does not want to run the model locally, what is the difference between these 2 models on the API?" and yet nobody can answer that question.