Why do we benchmark quants on perplexity and prose but never on tool call validity?

Reddit r/LocalLLaMA News
quantization benchmarking tool-call structured-output evaluation ai-agents

Summary

The article questions why quantization benchmarks focus on perplexity and prose quality instead of tool call validity, arguing that structured outputs degrade earlier due to fewer valid token continuations, which could mislead practitioners about usable quant levels for agentic use.

The mixed precision quant discussion here lately, MoE aware stuff that keeps shared experts and the edge layers at higher precision is great, but it's almost all measured against perplexity and general output quality. What I never see is structured output. Tool call JSON, function schemas, constrained formats. My intuition, and I'd like to be wrong, is that those degrade earlier than prose does. A model at Q4\_K\_M can still write a perfectly readable paragraph while quietly producing JSON that's a brace short or hallucinating a field name. Prose has a lot of valid continuations at each token. A schema has very few. So the same quant error that's invisible in text is fatal in a tool call. If that holds, then for agentic use the quant level you can actually get away with is lower than the perplexity charts suggest, and a lot of people are picking quants on the wrong metric. Has anyone benchmarked acceptance rate of valid tool calls across quant levels on one model? Not perplexity. Just did the JSON parse.
Original Article

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