What does it cost to process an image with a vision model? (blog.roboflow.com)

A recent analysis has detailed the varying costs associated with processing images using vision-language models (VLMs) like OpenAI GPT-5.5, Anthropic Claude Opus 4.7, and Google Gemini 3.1 Pro, highlighting the complexities of pricing in this domain. Unlike large language models (LLMs), which have straightforward pricing based on token counts, VLM costs are significantly influenced by how each provider tokenizes images, resulting in dramatic differences in pricing for the same image across different platforms. The analysis provides a structured approach for understanding the cost equation, showcasing that image tokenization methods—not just the price per token—lead to substantial variability. This breakdown is particularly significant for developers and organizations looking to implement VLMs in production scenarios, where scaling operations can become prohibitively expensive. It underscores the necessity for selecting models based on specific image characteristics and application requirements. The article further emphasizes that while frontier VLMs offer advanced capabilities, purpose-built models, optimized for specific tasks and settings, may ultimately provide more cost-effective solutions for high-volume processing tasks, thereby allowing users to balance the trade-off between generality and efficiency.