I hear the argument here, but isn't it possible it has dramatically more knowledge and when you get outside the common cases many of us use it for, it'll have completely different capabilities?

I feel like most benchmarks cluster on a reasonably limited area of human knowledge

It seems like there is tradeoff between model size and the need for tool use, which - in my mind - is quite costly in terms of time and tokens. More detailed world knowledge requires an exponential increase in model size, but most knowledge can be acquired ad hoc using search or database queries. This will fail for questions where the model lacks the knowledge to ask the right questions, but maybe this could be solved by a handful small inquiry models with different knowledge encoded in their weights?

I think the Western labs are burning through funding and compute to maintain the lead at any cost, efficiency be damned.

You can't compare models released 6+ months apart. GLM 5.2 was same architecture as 5.3 and not nearly as good. Takes time to build frontier intelligence and distill down to smaller sizes.

4.7 and 4.8 are smaller than 4.5 and 4.6

(And 4.5 was smaller than 4)

> that would prove rather embarrassing for Anthropic

Not really, in that you just work with different constraints.

Anthropic and US labs in general has maybe 100s to 1000s of GPUs per person to experiment. Zai and Chinese labs in general have 1-10.

The priorities are different.

My second favorite model by now is GLM 5.3 flash which is very capable of day to day task. I use it as the main model and GLM 5.3 for task that is more complex

Pretty much anything is better than Opus 5.

z.ai coder plan, both in opencode and direct API access. I use it for my side projects like https://github.com/scosman/Biscotti (on-device meeting transcription and summaries).