OpenRouter: Low-res images can cost more than high-res on reasoning models

在LLM中选择最佳图像输入细节级别

OpenRouter benchmarked image detail settings across five OpenAI and Google models on MMMU-Pro Vision. On gpt-5.5, low detail scored 65.2% vs 79.0% on auto, yet cost 5.1¢ per question vs 4.5¢—the model burned 1.6× more reasoning tokens trying to read blurry inputs, wiping out input savings. Non-reasoning models gpt-5.4-mini and gpt-4.1 did save money on low, but lost 9.7 and 17.4 accuracy points. Charts and graphs gained the most from auto detail: gemini-3.1-pro jumped from 78.6% to 91.7%. The post recommends sending clear images and dialing down reasoning effort instead.

Why it matters: OpenRouter benchmarked five models on MMMU-Pro Vision and found low-detail images make reasoning models more expensive—gpt-5.5 lost 14 points of accuracy and cost 13% more per question. Counterintuitive result backed by solid data, directly actionable for anyone tuning API cos...