This is it.

It's because they need subscription money and interaction data and so keeping a version bump in the wings to stop the bleeding from your competitor's version bump is the logical thing to do. It has nothing to do with RSI.

Of course they do. The real money makers are not subscription users, but the API users and you can just switch with the model selector.

Maybe process maturity too.

Like think about a software org with good CI/CD versus one without. The mature org can do consistent incremental releases because each one is safe and low overhead, the messier org will do fewer big releases because each release requires a big effort on its own.

As model developers mature we might expect to see more frequent point releases rather than the big bang evolutions.

Probably one of the factors. Signed up to openai pro a few days ago, deciding between openai and anthropic, then sonnet 5.5 was released and am wondering whether I made a mistake.

Luckily it's not a mistake as now we have access to . . . dots.

(and sol 6.1, it seems)

jokes on me, I pay for all the subscriptions.

It feels like an updated 4.6. It's fantastic.

> I'm not the only one

See, that's an/the issue. As soon as people start to flee to the improved model, they start to serve degraded models to keep up with the demand.

Which paper are you referring to?

What's that in summary?

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and seems like they're claiming Sol/Opus are not frontier (and only Astra/Fable are)

The old "the bigger number is better", GPT announces model 6.1, the obvious thing to do next is to announce Gemini 27, and after that Claudé 3000, then a flute album.

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If you look closely at various benchmarks, you'll see that often models will improve in certain areas while regressing in others. It suggests we're already at the point of diminishing returns.

I can't recommend Chinese models enough. My personal favorite is DeepSeek v4.1 Flash but I have tried Qwen 3.8, Kimi 3 and GLM 5.3 which are equally impressive but DeepSeek is the cheapest and fastest regularly hitting 270 token per second.

And yeah I have worked with Anthropic and OpenAI models, they're good but they cost a fortune while Chinese models are already really good at a fraction of the cost.

I keep hearing about these Chinese models, but what exactly are you doing with the models and coding? I have a need to fully write code with full tool calling capabilities. Not just methods or functions. I want to be able to prompt a feature and it makes the JIRA ticket, and fully implements it and makes a PR. I don't want to babysit it or even read the code. Once it creates the PR, I want it to monitor it for any comments fro Copilot/security review and then fix it as necessary.

Is that what the Chinese models are capable of? If so, how are you using them? API? Or is there an inference provider that is as fast as the big 2? What about the coding harness?