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Images sent to vision APIs can consume billable input tokens, but there is no universal pixels-to-tokens formula. Each provider and model may resize an image, divide it into patches or tiles, and apply its own accounting rules. To estimate cost, check the current documentation for the exact model and detail setting you plan to use.
Why image dimensions affect token use
Image dimensions matter because a provider may resize an image and then count the patches or tiles needed to represent it. Raw pixel count alone does not tell you how many tokens an image will use. Image tokens can contribute to both input charges and throughput limits, and the applicable calculation depends on the provider and model.
For a useful estimate, identify the model and version, image detail or fidelity setting, processed dimensions, patch or tile count, and input-token rate. Apply that provider’s billing rules rather than comparing token counts across providers as if they represented equivalent work.
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How providers account for images
OpenAI: patch-based and base-plus-tile methods
OpenAI documents different image-accounting methods across model families. In its patch method, the selected detail level sets dimension limits; the image is resized while preserving its aspect ratio, then counted in 32 × 32 patches. If a patch budget applies and the image exceeds it, the image is reduced proportionally and the patch count is recalculated. The model-specific multiplier is then applied.
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For the documented gpt-6-astra high-detail example, the patch budget is 2,500 and the multiplier is 1.2×. A 1024 × 1024 image produces 1,024 patches and an estimated 1,229 image input tokens. A 2048 × 2048 image is reduced to 1600 × 1600 to fit the budget and is estimated at 3,000 tokens. These are examples for that model and setting, not a general image-token rate. OpenAI notes that billing can differ by one token because of floating-point rounding. See OpenAI’s image and vision guide.
For other model families, OpenAI also documents base-plus-tile accounting. Its guide says low detail uses the model’s base token count regardless of image dimensions. In high or auto detail, the image is scaled to fit within a 2048 × 2048 square, a shortest-side limit is applied, and the image is counted in 512-pixel squares; the associated tile tokens are added to the base. The base and tile counts vary by model, so check the current model table rather than reusing a figure from another family.
Google Gemini: 384-pixel threshold and 768-pixel tiles
Google’s Gemini API documentation says an image no larger than 384 pixels on both dimensions counts as 258 tokens. Larger images are divided into 768 × 768-pixel tiles, each counted at 258 tokens. This is a Gemini-specific rule; confirm the current model and API documentation before using it for a production estimate. See Google’s Gemini token documentation.
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Anthropic describes images as being processed in 28 × 28-pixel blocks called visual tokens. Its guidance recommends downsampling when high-resolution fidelity is unnecessary, while noting that computer use, screenshot understanding, and dense documents may benefit from higher resolution. See Anthropic’s vision documentation.
What an image-token estimate does—and does not—tell you
OpenAI’s calculator provides a reproducible example: for one 1024 × 1024 image, it displays 1,229 tokens and $0.01229 under the selected model and standard input-rate assumptions. That dollar figure is specific to the calculator’s selection and assumptions, not a general price for sending an image. The calculator estimate is per image and excludes other prompt tokens, output tokens, caching, long-context pricing, and data-residency adjustments; billing may also differ by one token because of rounding. See OpenAI’s pricing page and calculator.
For a request-level cost estimate, account for the text prompt and generated output as well as image input. Include caching, long-context pricing, data-residency adjustments, or other applicable charges where relevant. An image-only token estimate is not the full request bill.
How to compare image costs fairly
Compare the same kind of request, not just the displayed token totals. A token count from one provider is not automatically comparable with the same count from another, because each provider applies its own image processing and billing rules.
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- Model and version: Record the exact model whose rules and rates you are applying.
- Image handling: Note the detail or fidelity setting, dimensions after resizing, and patch or tile count when the provider exposes them.
- Rates and scope: Use the relevant input-token rate and distinguish an image-only estimate from the full request cost.
- Additional billing factors: Check whether caching, long-context pricing, data-residency adjustments, or other charges are included or excluded.
Choose image detail for the task
Use lower detail or a smaller image when the task does not depend on fine visual information. Broad scene description may not require the original resolution. Small text, dense documents, screenshot interaction, and precise visual coordinates can require more detail. OpenAI advises choosing high detail when original resolution or precise image coordinates are needed; Anthropic advises downsampling when extra high-resolution fidelity is unnecessary. These are provider recommendations, not a guarantee that downsampling will preserve accuracy for every image or task.
Quick Recap
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- Identify what the model must read or locate: overall scene, small text, dense content, or precise coordinates.
- Choose the lowest detail setting and image dimensions that can reasonably preserve that information.
- Estimate image tokens using the chosen provider’s current documentation or calculator and exact model settings.
- Estimate the full request separately, including text, output, and applicable billing adjustments.
- If reducing resolution, verify that the resulting image still supports the task before relying on its answer.
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