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arXiv · 2604.02492

Token-Efficient Multimodal Reasoning via Image Prompt Packaging

Joong Ho Choi·Jiayang Zhao·Avani Appalla·Himansh Mukesh·Dhwanil Vasani·Boyi Qian
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Abstract

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.

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Joong Ho Choi, Jiayang Zhao, Avani Appalla, Himansh Mukesh, Dhwanil Vasani, Boyi Qian. 2026-04-02. Token-Efficient Multimodal Reasoning via Image Prompt Packaging. https://arxiv.org/abs/2604.02492

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