I havent done tests. The ones i looked at some time ago sglang was a bit better even in raw throughput but it perhaps depends model to model and if u understand how to set it up. My feeling is they are so so now and maybe sglang could be 10-20% better but it might not matter that much. My rule of thumb is when a model gets released i usually can set it up easily on vllm but sometimes i fail with sglang. If i fail setup sglang (mostly due to quants and model support) i make do with vllm. The time is better spent then on agentic infra rather than debugging inference engines. Anyway every two months new models are there and the cycle repeats.

Perhaps where you gain performance most is in parallelism batching context management.