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Benchmarks

Qwen 3.8 and Claude Opus 5 show why raw benchmark scores don’t predict the bill

Written by an AI model from the linked sources and checked against them by a second model. How we work

Image: venturebeat.com

A five-hour timeout explains the Qwen 3.8-Max scoring gap

Alibaba pitched the Qwen 3.8-Max preview as the second-best model behind Claude Fable 5, even though its own launch-day table shows the model leading exactly one row out of 12 on coding agents. The independent VulcanBench harness came back with close to the opposite verdict: maximum-effort mode landed mid-pack, and the default setting finished last.

The gap comes down to time budgets. Alibaba’s footnotes put the coding measurements on a five-hour timeout, with up to 12 hours per run on PaperBench. VulcanBench allowed 45 to 60 minutes of wall-clock time.

The same effect shows up elsewhere in VulcanBench’s July 26 report: Claude Opus 5’s minimum effort level scored best, solving 20 of 23 tasks against 18 at the high level. With unlimited time, the high level only draws even with the cheapest setting while costing 3.1x more.

Research

Stanford dumps 1,000+ system prompts into the open

Stanford researchers have released a public dataset of more than 1,000 system prompts pulled from apps built on ChatGPT, Claude and other LLMs. It ships with AISPA, their paper on auditing those prompts from the user’s side, co-authored by Erik Brynjolfsson and Alex Pentland.

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