Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text
  Inputs in Multimodal LLMs
Large language models (LLMs) and their multimodal variants can now process
visual inputs, including images of text. This raises an intriguing question:
can we compress textual inputs by feeding them as images to reduce token usage
while preserving performance? In this paper, we show that visual text
representations are a practical and surprisingly effective form of input
compression for decoder LLMs. We exploit the idea of rendering long text inputs
as a single image and provide it directly to the model. This leads to
dramatically reduced number of decoder tokens required, offering a new form of
input compression. Through experiments on two distinct benchmarks RULER
(long-context retrieval) and CNN/DailyMail (document summarization) we
demonstrate that this text-as-image method yields substantial token savings
(often nearly half) without degrading task performance.
68f999f8b9b2e4ae04673844
Yanhong Li
claimed_verified
2025-10-23T14:47:58.299Z
68c855762302522c5693dd92
/avatars/c03c8eba0a21566996502a200f83285b.svg
Yanhong Li
user
yanhong-l
68f999f8b9b2e4ae04673845
Zixuan Lan
68f999f8b9b2e4ae04673846
Jiawei Zhou
Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text
  Inputs in Multimodal LLMs
Large language models (LLMs) and their multimodal variants can now process
visual inputs, including images of text. This raises an intriguing question:
can we compress textual inputs by feeding them as images to reduce token usage
while preserving performance? In this paper, we show that visual text
representations are a practical and surprisingly effective form of input
compression for decoder LLMs. We exploit the idea of rendering long text inputs
as a single image and provide it directly to the model. This leads to
dramatically reduced number of decoder tokens required, offering a new form of
input compression. Through experiments on two distinct benchmarks RULER
(long-context retrieval) and CNN/DailyMail (document summarization) we
demonstrate that this text-as-image method yields substantial token savings
(often nearly half) without degrading task performance.
Rendering text as images reduces token usage for decoder LLMs without compromising performance on tasks like long-context retrieval and document summarization.
large language models multimodal variants visual inputs text-as-image input compression decoder tokens RULER CNN/DailyMail token savings