It doesn’t literally mean “90% failure rate vs 100%”

In the HLE benchmark, for example, we may be talking about 45% test score for MiMo Pro or GLM or something, vs 55% for Sonnet and 61% for maxed out Claude.

But further complicating that, those particular open weight models do better than Claude in one particular hallucination benchmark, meaning Claude/ChatGPT might be worse at RAG/extraction type workloads. It’s like this across the board, with different models trading blows in different areas.

By “90%,” he really means “it’s so close the better model depends on the particular application, workload, harness, sampling configuration” and such, and that none of these models are close to “100% accuracy” across the board.

One other thing: Anthropic, OpenAI, and Grok have all been accused of “dumbing down” models under high demand to speed up inference. If your application depends on them, and they are the only provider, that could throw a wrench in the workflow.

But open weights? Pick your provider. You get an API as consistent as you are willing to pay for.

In other words, that “100%” isn’t really representing the potential inconsistency one gets being locked to a single provider.