OpenRouter publishes guide to benchmarking LLMs on application-specific tasks
Overview
OpenRouter's blog explains how to choose an LLM by running tasks drawn from an application's own inputs and output formats, graded by code rather than judged by hand.
The guide describes running identical tasks through OpenRouter and comparing graded accuracy, billed cost and request time. In OpenRouter's own 60-task synthetic study, GPT-6.1 Sol passed 180 of 180 trials, DeepSeek had the lowest response cost at 157 of 180 passes, and the public ranking agreed only weakly with the study results (Spearman correlation 0.109).
The companion Python example runs offline checks that make no API calls. Paid runs require an explicit --live flag. The bundled full-study sandbox and native setup support Windows x64 only.
Written by AI from the articles below · updated Nov 11, 7:33 PM ET
Check the sources:
Article timeline
The articles in this story. Times are ET.
- OpenRouter BlogOfficialOpenRouter explains how to benchmark LLMs with code-graded, app-specific tasks
AIOpenRouter says LLM choices should rest on tasks drawn from an application's own inputs and output formats, graded by code. In its 60 synthetic tasks, GPT-6.1 Sol passed 180/180 trials, DeepSeek had the lowest response cost at 157/180 passes, and the public ranking agreed weakly with a Spearman correlation of 0.109. The companion Python example runs offline checks, but the full-study sandbox supports Windows x64 only.
Heat Trend
- Current comparable heat
- 7
- Peak comparable heat
- 10Nov 11, 8:00 PM ET
- Change in past 24 hours
- –
The trend compares only continuously observed participants, so its scope may be smaller than the current heat total. Hover or tap for hourly heat; use arrow keys with a keyboard.