Summary
SGLang said GLM-5.3-Flash initially generated about twice as many reasoning tokens on open-source engines as on Z.ai’s API on AIME and GPQA, despite similar scores. Fireworks, vLLM, SGLang and Z.ai then reran tests and updated the serving path; SGLang said the excess was concentrated in some reasoning prompts and was not seen on the cited agentic benchmarks.
Why it matters
Serving-stack behaviour can materially change an AI model’s cost and latency, making inference engineering part of the practical capability story. The account is an engineering report rather than an independent benchmark paper.
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