Vision Token Counts: What does it cost to process an image with a frontier vision model?

The complexities of pricing vision-language models (VLMs) compared to language models (LLMs) are explored by examining how different providers tokenize images, impacting cost estimates. Unlike LLMs, where cost calculation is straightforward by counting input and output tokens, VLMs require consideration of how images are transformed into tokens, with significant variability between providers such as OpenAI's GPT-5.5, Anthropic's Claude Opus 4.7, and Google's Gemini 3.1 Pro. Each provider employs distinct methods: GPT-5.5 uses patch-based tokenization, Claude applies an area-based formula, and Gemini uses fixed-cost image tiles, leading to varied costs for the same image across platforms. Price comparisons at different image sizes reveal that Claude is cost-effective for smaller images, while Gemini and GPT-5.5 are more competitive for larger ones. The article highlights that while frontier VLMs are beneficial for low-volume tasks requiring general reasoning, they become cost-prohibitive at scale, where specialized models like RF-DETR may offer more efficient solutions for targeted tasks.