# Token-Efficient Multimodal Reasoning via Image Prompt Packaging

arXiv: https://arxiv.org/abs/2604.02492 · HTML: https://mlsanity.com/paper/2604.02492/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 13 of 13)

- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://mlsanity.com/paper/2305.13707/) (2023)
- [DeepSeek-OCR: Contexts Optical Compression](https://mlsanity.com/paper/2510.18234/) (2025)
- [MLLMs are Deeply Affected by Modality Bias](https://mlsanity.com/paper/2505.18657/) (2025)
- [Mitigating Modality Collapse in Multimodal VAEs via Impartial Optimization](https://mlsanity.com/paper/2206.04496/) (2022)
- [A Closer Look at Multimodal Representation Collapse](https://mlsanity.com/paper/2505.22483/) (2025)
- [Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model](https://mlsanity.com/paper/2402.19150/) (2024)
- [When Language Overrules: Revealing Text Dominance in Multimodal Large Language Models](https://mlsanity.com/paper/2508.10552/) (2025)
- [Texture or Semantics? Vision-Language Models Get Lost in Font Recognition](https://mlsanity.com/paper/2503.23768/) (2025)
- [Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text Inputs in Multimodal LLMs](https://mlsanity.com/paper/2510.18279/) (2025)
- [Some Modalities are More Equal Than Others: Decoding and Architecting Multimodal Integration in MLLMs](https://mlsanity.com/paper/2511.22826/) (2025)
- [MMLF: Multi-modal Multi-class Late Fusion for Object Detection with Uncertainty Estimation](https://mlsanity.com/paper/2410.08739/) (2024)
- [Read or Ignore? A Unified Benchmark for Typographic-Attack Robustness and Text Recognition in Vision-Language Models](https://mlsanity.com/paper/2512.11899/) (2025)

## Abstract (arXiv)

Deploying large multimodal language models at scale is constrained by token-based inference costs, yet the cost-performance behavior of visual prompting strategies remains poorly characterized. We introduce Image Prompt Packaging (IPPg), a prompting paradigm that embeds structured text directly into images to reduce text token overhead, and benchmark it across five datasets, three frontier models (GPT-4.1, GPT-4o, Claude 3.5 Sonnet), and two task families (VQA and code generation). We derive a cost formulation decomposing savings by token type and show IPPg achieves 35.8--91.0% inference cost reductions. Despite token compression of up to 96%, accuracy remains competitive in many settings, though outcomes are highly model- and task-dependent: GPT-4.1 achieves simultaneous accuracy and cost gains on CoSQL, while Claude 3.5 incurs cost increases on several VQA benchmarks. Systematic error analysis yields a failure-mode taxonomy: spatial reasoning, non-English inputs, and character-sensitive operations are most vulnerable, while schema-structured tasks benefit most. A 125-configuration rendering ablation reveals accuracy shifts of 10--30 percentage points, establishing visual encoding choices as a first-class variable in multimodal system design.