Multimodal (Image + Text) Token Cost Calculator

Multimodal (vision-enabled) large language models process images by first converting them into a token representation the underlying transformer architecture can handle alongside regular text tokens, and understanding this conversion is the key to accurately budgeting any feature that sends images to an LLM, since images very often contribute the majority of a multimodal request's total token cost, not text.

The tokens-per-image figure is genuinely provider- and resolution-specific rather than a universal constant. Providers commonly tile or resize an input image according to specific internal rules before tokenizing it, and a higher-resolution image typically consumes meaningfully more tokens than a lower-resolution version of the same content, since more visual detail requires more tokens to represent. This is exactly why checking a specific provider's documented tokens-per-image convention for your actual expected image sizes — rather than assuming a single flat figure applies universally — matters for an accurate cost estimate, and why this calculator exposes tokens-per-image as a directly adjustable input rather than baking in a single fixed assumption.

The practical cost implication that surprises many teams building a vision-enabled feature for the first time is just how much images contribute relative to text: a single image commonly consumes several hundred to over a thousand tokens depending on resolution and provider, easily matching or exceeding the token cost of several paragraphs of text. For a use case processing many images per request — analyzing a multi-page document where each page is sent as a separate image, for instance — this per-image cost multiplies directly by page count, very often becoming the dominant driver of total request cost, with the accompanying text instructions and any generated text output contributing comparatively little by contrast.

Some providers offer a reduced-detail or lower-resolution processing mode specifically to reduce this cost for use cases that don't need full image fidelity — simple visual classification (is this image a cat or a dog) genuinely doesn't need the same level of visual detail as fine-grained document text extraction, and checking whether a lower-detail mode is available and sufficient for a specific use case's actual accuracy requirements is a legitimate, often substantial cost optimization worth exploring before defaulting to full-resolution processing for every image sent.

For any use case where the actual information content of an image could instead be captured as text — a scanned document already run through OCR, a chart whose underlying data is available in tabular form — comparing the token cost of sending the raw image against sending the already-extracted text equivalent is worth doing directly, since text tokens are typically dramatically cheaper per unit of actual information conveyed than the equivalent image tokens, making a text-based pipeline the more cost-effective choice whenever the underlying use case allows for it.