The Hidden Token Tax on JSON Schemas

Nimrod Taiblum of Vonage examines the often-unreported input-token costs of using JSON schemas for structured LLM output, finding that schemas can substantially increase billing for short, high-volume tasks such as classification, routing, and entity extraction. Tests using five schema complexities with Gemini 2.5 Flash, Gemini 3.5 Flash, and Claude Haiku 4.5 showed that overhead rises with schema complexity but varied sharply by model: Gemini 2.5 Flash remained near the prompt-only baseline, while Claude Haiku 4.5 reached 1,189 input tokens for a deeply nested schema versus 186 for Gemini 2.5 Flash. For a roughly 100-token prompt with a medium schema, reported costs per million calls ranged from $37.20 for Gemini 2.5 Flash to $396 for Gemini 3.5 Flash, reflecting both token prices and differing schema handling. The analysis suggests that schema overhead becomes relatively insignificant for long inputs but can dominate costs for prompts under 500 tokens, and recommends minimizing and flattening schemas, placing explanatory details in prompts where appropriate, batching requests, considering schema-free JSON prompting for trivial outputs, and measuring schema-related token differences when selecting models and estimating production costs.

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