What's very promising here is the number of tokens-vs-accuracy ratio. I am assuming their "output tokens" means tokens generated as part of thinking and any tool calls (what are referred to as "input tokens" from billing PoV by service providers). The Chinese models like Qwen3.8 and GLM 5.2 are insanely overthinking in our workloads (which are highly complex data analysis tasks). It's a factor of 3-4x over Opus and GPT models. Even with cheaper prices per 1M tokens, the cost ends up being higher, in some cases 2x. So this is very promising from GLM 5.3. Looking forward to trying it.