- In Gemini at least, if you look at how they process PDFs, they do an OCR and then feed the text + image to the model, without charging you for the text tokens (I believe).
So my guess is that Claude’s backend is doing the same — so this hack is probably more of a loophole in token accounting that might get closed if Claude is doing what Gemini does
- I tried the same thing last year (with openai models), back then it worked to reduce prompt tokens, but you needed way more completion tokens, ultimately more expensive (and slower) https://pagewatch.ai/blog/post/llm-text-as-image-tokens/
- This seems like a pricing hack that burns resources, that when the loophole gets closed the price of OCR will have to rise?
- It’s not a loophole, it just happens that encoding information as optical tokens is much more efficient than text.
- Of course it isn't
A text encoding uses 8bits per character on average, tokenization further compresses that
An image font would be 25 bits if 5x5, and most fonts are 12 pixels high
Of course it isn't efficient, this is a pricing inefficiency and a hack to exploit it (even the author describes it as an exploit)
- Truly a picture is worth a thousand words.
- Not really. They arent actually using more resources this way either. This might be a fundamental inefficiency thats being removed
It kinda makes sense too. Because while people do read code word by word, we often "glance over" it and do roughly pattern recognition on it to know what it does. Only homing in on something when we need to answer a specific question. I think humans kinda naturally do this exploit anyway
- That is hilarious and an amazing find.
- there's also a DeepSeek whitepaper on this technique https://www.seangoedecke.com/text-tokens-as-image-tokens
- I want to see more text-free foundation models
38 points by dimitropoulos 2 hours ago | 12 comments