Vision AI cost guide
Image Token Cost Guide for Vision AI
Vision requests are not priced from text alone. Estimate image count, resolution, detail level, prompt text, output length, retries, and monthly volume before sending screenshots, receipts, product photos, or scanned pages to an AI model.
Separate image input from text input
A vision request usually has three cost buckets: image input tokens, text prompt tokens, and generated output tokens. Keep those buckets separate because changing the image size, detail setting, or number of images can matter more than rewriting a short prompt.
For a monthly estimate, calculate one representative request first, then multiply by images per request, workflow rounds, retry rate, and requests per day. If users upload batches, estimate the large batch separately from the average upload.
Resolution and detail change the estimate
Provider image token rules are model-specific. Some OpenAI vision models use a low-detail option for a fixed low token count and a high-detail option that counts resized image tiles. Claude vision models describe image cost in visual patches. Gemini token counting can depend on media resolution, tiles, or the final usage metadata returned after execution.
Do not assume a 4 MB image is cheaper than a 1 MB image or that JPEG compression alone reduces token cost. Pixel dimensions, provider resizing rules, detail level, and final model accounting are the important inputs.
Planning example
A support workflow reviews three product photos, sends a 500-token prompt, and writes a 700-token answer. If the image detail setting doubles the image-token estimate, monthly cost can change more than it would from trimming 100 words from the prompt.
Reduce vision cost before launch
Resize images to the smallest dimensions that still preserve the needed evidence, crop irrelevant borders, use low detail when exact small text is not required, limit the number of images per request, and avoid resending the same image in every workflow round. For documents, compare OCR text extraction against sending page images directly.
If the model only needs a barcode, table, chart label, or visible defect, crop to that region before calling the API. If the model needs layout or handwriting, keep a higher-detail path and test accuracy before reducing resolution.
Validate with real usage metadata
Vision billing rules change and exact token counts can differ by model. Before relying on an estimate, run a small sample and compare it with the API usage metadata or provider dashboard. Recheck the official OpenAI image and vision guide, Claude vision guide, and Gemini token counting guide.
Next step
Use the calculator text mode for the prompt and expected answer, then add your measured image-token estimate as part of the input-token total. If the same images or extracted text are reused across requests, compare the result with the prompt caching cost guide.