I apologize in advance for this crash out, but holy shit I'm so tired. I'm trying to figure out what is even being measured here and it's nearly impossible. I read the post. It provided no insight whatsoever into how these "tests" work. Things that were not mentioned: - Harnesses used - Tasks used - How many times tasks are run - What is done to identify variance in daily runs - What analysis is done on "bad" runs to detect root causes for failures - What APIs are being used (matters a LOT) - How the +/- 10% "variance" was selected - Why tokens and costs are "weighted" the same, and combined rank roughly as high as intelligence - Why tokens are included at all when costs are the metric that matters I'm sorry @bridgemindai - am I missing something here? I just want to make sure I understand fully before building my own alternative bench. I DM'd you asking for traces from your runs, would be super helpful as I start digging deeper here.

Introducing GLM-5.3-Flash - Leading capabilities at a highly competitive price - Natively multimodal with a 1M-token context window - A 320B-A18B model released under the MIT License - Previously previewed as Ox Alpha, running entirely on Chinese AI chips Blog: Available now across all official platforms: Weights: API: Coding Plan: ZCode: Chat: AutoClaw:

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