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⌈w/28⌉×⌈h/28⌉\lceil w/28 \rceil \times \lceil h/28 \rceil

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OpenAI’s newer patch-tokenized models cover an image with 32-pixel-square patches and cap the total patch count per image, resizing proportionally down to fit the cap when an image would otherwise exceed it — for example, resizing an 1800-by-2400-pixel image down to 1056 by 1408 pixels to land at 1,452 patches against a 1,536-patch budget — while a detail: low setting instead fixes the cost at 85 tokens regardless of the original size by resizing to 512 by 512 first, and an original or auto detail setting preserves full resolution for coordinate-sensitive tasks at correspondingly higher token cost [ 6 ] . Anthropic’s documentation states the rule for Claude directly: an image costs ⌈\lceil…

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⌈w/28⌉×⌈h/28⌉\lceil w/28 \rceil \times \lceil h/28 \rceil

Equation 15 · Foundation Models

Building a Multimodal AI Application That Actually Uses Its Inputs

This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text.

OpenAI’s newer patch-tokenized models cover an image with 32-pixel-square patches and cap the total patch count per image, resizing proportionally down to fit the cap when an image would otherwise exceed it — for example, resizing an 1800-by-2400-pixel image down to 1056 by 1408 pixels to land at 1,452 patches against a 1,536-patch budget — while a detail: low setting instead fixes the cost at 85 tokens regardless of the original size by resizing to 512 by 512 first, and an original or auto detail setting preserves full resolution for coordinate-sensitive tasks at correspondingly higher token cost [ 6 ] . Anthropic’s documentation states the rule for Claude directly: an image costs ⌈\lceil…

Meanings in this article

  • hh: the image’s pixel dimensions after any provider-side resize.
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