Predict the total vision token count for a tiled high resolution image
A VLM uses AnyRes tiling. A high resolution image is split into a 3x3 grid of tiles, and one additional downscaled global view of the whole image is added. The vision encoder emits 256 tokens for each tile and 256 tokens for the global view. How many vision tokens does this single image contribute to the LLM's context?
A 3x3 grid is 9 tiles plus 1 global view, so 10 encoder passes at 256 tokens each gives 2,560 vision tokens for one image.
Imagine cutting a big poster into a 3-by-3 grid of squares, then also keeping one shrunk-down copy of the whole poster. Now count how many pieces a reader has to look at: nine squares plus one whole-poster thumbnail makes ten pieces. The reader writes the same number of notes, 256, for every single piece. So you just multiply: ten pieces times 256 notes each equals 2,560 notes for this one poster. The mistake people make is forgetting that 3-by-3 means nine pieces, not three, or forgetting the extra thumbnail. Count the pieces carefully, multiply, done.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
On the surface this is a one-line multiplication. The reason interviewers use it is that the two easy slips, miscounting the grid and dropping the global view, reveal whether a candidate actually pictures the tiling mechanism or just memorized that images are expensive.
The arithmetic also forces a magnitude check. Most people who have only used a chat UI have no feel for how many tokens an image costs. Producing 2,560 from a plausible scenario plants that number, and 2,560 tokens for one image reframes how you think about prompt budgets.
This walkthrough does the count carefully, generalizes it into a formula so you can predict any tiling configuration, shows how the cost scales with resolution, and closes on what those tokens do downstream in prefill and the KV cache. The goal is that you can both compute the number and explain why it is the number that matters.
Counting the encoder passes correctly
The whole problem reduces to one question: how many separate things does the encoder run on, and what does each contribute?
The grid. A 3x3 grid means 3 rows and 3 columns. The tile count is rows times columns, so 3 x 3 = 9 tiles. This is the first slip: people read 3x3 and answer 3, or split the difference and guess 6. It is an area, so it is the product, not the sum or one of the factors.
The global view. AnyRes adds one downscaled view of the whole image alongside the tiles. Its job is to give the model the big picture that the individual tiles lose. It is a separate encoder pass, so it adds one to the count. This is the second slip: people compute the grid correctly and forget this extra block.
So the pass count is 9 + 1 = 10. Every pass here emits the same fixed 256-token block, which simplifies the final step to a single multiply. In real systems the global view sometimes uses a different token count than the tiles, but in this scenario they match, so you do not have to track them separately.
Nail the count and the rest is arithmetic. The discipline is to always write tiles as rows times columns and to always add the global view before you multiply.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
Real products, models, and research that use this idea.
- LLaVA-NeXT AnyRes splits a high-resolution image into a grid of tiles plus a global view, each tile emitting its own block of vision tokens just like this scenario.
- OpenAI GPT-5.5 high-detail images are priced as a base block plus per-tile blocks, so a large image lands in the thousands of tokens.
What an interviewer would ask next. Try answering before peeking at the approach.
QWhat is the token count if the grid grows to 4x4 with the same global view and per-tile block?
4x4 is 16 tiles plus 1 global view, so 17 passes. 17 x 256 = 4,352 tokens. Notice the cost grows with the square of the linear resolution increase because tiles cover area.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Reading 3x3 as 3 or 6 tiles instead of 9, or forgetting the global view adds one more block. Both undercount the total.
60 second bullets to scan on the way to the call.
How many tiles does a 3x3 grid contain, and why is it rows times columns?
What does the downscaled global view add to the total pass count?
Primary sources. Browse if you want the original framing.
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