Choosing the Optimal Image Input Detail Level in LLMs

The exploration of image input detail levels in large language models (LLMs) such as OpenAI and Google's latest models reveals that using higher detail levels, like auto detail, generally yields better results and can sometimes be more cost-effective compared to lower detail levels. Benchmark tests showed that models like gpt-5.5 perform significantly better with auto image detail, achieving higher accuracy and lower costs per question due to reduced reasoning effort. Conversely, low detail levels force models to exert more reasoning effort, which increases output token costs and diminishes overall accuracy. Non-reasoning models such as gpt-5.4-mini benefit from low detail due to lower costs and faster response times, although at the expense of accuracy. The study suggests that for reasoning models, maintaining higher detail levels and adjusting reasoning efforts are more effective strategies for optimizing performance and cost.

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