Image pixels can count as input tokens in vision APIs, but there is no universal pixels-to-tokens formula. Each provider—and sometimes each model—may resize images and count them using patches or tiles. To estimate cost or usage limits, check the current documentation for the model and image-detail setting you plan to use.
Why image dimensions affect token use
An API may resize an image before processing it, then account for the resulting image using model-specific patches or tiles. A larger image can therefore use more image input tokens, but its raw pixel count alone does not reveal the token total. The provider’s processing rules determine the estimate.
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Image tokens can count toward billable input and throughput limits. They are only part of a request: text in the prompt, generated output, and applicable caching or context charges can affect the total as well.
How providers count image input
OpenAI: patches and model-specific accounting
OpenAI documents different image-accounting methods across model families. In its image and vision guide, one high-detail example for gpt-6-astra applies a 2,500-patch budget and a 1.2× multiplier. A 1024 × 1024 image produces 1,024 patches of 32 × 32 pixels and an estimated 1,229 image input tokens. Those figures describe that model and example, not a general rate for images.
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In the same example, a 2048 × 2048 image is proportionally reduced to 1600 × 1600 to fit the patch budget and is estimated at 3,000 tokens. Floating-point rounding can make the billed figure differ by one token.
OpenAI also documents base-plus-tile accounting for other model families. For those models, low detail uses the model’s base token count regardless of image dimensions. High or automatic detail follows a different process involving scaling, a shortest-side limit, 512-pixel squares, and tile tokens added to the base. The counts vary by model, so consult the current model table rather than carrying figures over from another model.
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Google Gemini: 384-pixel threshold and 768-pixel tiles
Google’s Gemini token-counting documentation says that an image no larger than 384 pixels in either dimension—and thus at or below 384 pixels on both dimensions—counts as 258 tokens. Larger images are divided into 768 × 768 pixel tiles, each counted at 258 tokens. These are Gemini-specific rules; verify the current model and API documentation before using them for a production estimate.
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Anthropic describes images as 28 × 28 pixel blocks called visual tokens and recommends downsampling when high-resolution fidelity is unnecessary. Its guidance identifies computer use, screenshot understanding, and dense documents as cases where higher resolution may matter. See the Claude vision documentation.
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How to estimate the cost of an image request
- Choose the provider and model. Image handling and token accounting can differ across providers and model families.
- Set the image detail or fidelity level. Use the setting you intend to send, since detail can change processing and token use.
- Check the dimensions the provider processes. Account for any resizing, patch budget, or tile rules in the model’s documentation.
- Use the provider’s current calculator or token-counting guidance. Treat its result as an estimate with the assumptions and exclusions stated by that provider.
- Estimate the whole request. Add relevant prompt and output usage, and account for caching, long-context pricing, data-residency adjustments, or other applicable charges.
OpenAI’s token calculator displays an example of 1,229 tokens and $0.01229 for one 1024 × 1024 image under its selected model and standard input-rate assumptions. The estimate is per image, not a general price for any image or model. OpenAI says the calculator excludes other prompt tokens, output tokens, caching, long-context pricing, and data-residency adjustments; billing can also differ by one token because of rounding. Check the calculator’s assumptions before applying its estimate to a request.
How to reduce image token use without losing needed detail
- For broad scene description, try lower detail or a smaller image when fine features are not important.
- For small text or dense documents, retain enough resolution for the text to be legible.
- For screenshot interaction or precise visual coordinates, use the fidelity the task requires; reducing resolution may remove useful detail.
Lower detail and downsampling can reduce image input, but they are not guaranteed to preserve accuracy. Check the result against the task—especially when small elements or exact positions matter—rather than assuming a smaller image is equivalent.
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How to compare estimates across providers
A token count by itself is not a cost comparison. Compare the same intended task and note the provider, model and version, input-token rate, detail setting, processed image dimensions, patch or tile count, and other billable prompt or output usage. Then account for each provider’s relevant billing rules, including any caching, long-context, or data-residency adjustments. The same number of tokens can have different cost implications under different provider rates and rules.
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