The following optimization strategies can be used when performing multi-model comparison tests via OpenBench:
- utilization
--max-connectionsParameter to adjust the number of concurrent requests (default 10), set reasonably according to the API quota - right
bench evalCommand Usage--modelMultiple parameter values are tested simultaneously for multiple models, e.g:--model groq/llama-3.3-70b openai/o3-2025-04-16 - pass (a bill or inspection etc)
--limitRun a small sample test (e.g., 50 bars) first to verify the correctness of the process before running it at full volume - For the billing API model, the fit
--jsonOutput intermediate results to prevent unintended interruptions - Cache the results of the high-frequency test model into the
./logs/directory, by means of thebench viewMake a side-by-side comparison
This answer comes from the articleOpenBench: an open source benchmarking tool for evaluating language modelsThe
May not be reproduced without permission:AI productivity tools " How to optimize the efficiency of parallel evaluation of multiple models?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil