Pixels for Programs? A Cross-Provider Case Study of Input-Token Accounting for Source Code as Text and Images
Long source-code contexts consume many text tokens, motivating the proposal to render code as images for vision-language models. Recent work asks whether models can still solve code tasks after this transformation. We examine a different systems question: how commercial APIs count the resulting requests. We present a reproducible measurement case study of provider-reported input tokens for raw source text and a compact rendered-image representation. The benchmark pairs requests across five programming languages, nine source lengths from 20 to 2,000 lines, and 15 available model aliases exposed by Anthropic, OpenAI, and Google Vertex AI. These aliases collapse to approximately five distinc...
| Authors | Ronak Bhalgami |
|---|---|
| Primary category | cs.SE |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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