Gemini 2.5 Pro vs GPT-5.4 for Structured Output

What Structured Output demands: strict JSON schema compliance, format adherence, stable field ordering and types, and reliable error handling or refusal when inputs violate schema. The benchmark we use (structured_output = "JSON schema compliance and format adherence") is the primary task signal. In our tests both models achieve a perfect 5/5 on structured_output, meaning they produce schema-compliant outputs across our cases. Supporting capabilities that explain differences: tool_calling (function/argument selection and accuracy) helps generate properly typed fields when outputs feed downstream systems — Gemini scores 5 vs GPT-5.4's 4. Faithfulness and long_context are both 5 for each model, supporting consistent schema across large documents. Classification (routing/label mapping) favors Gemini (4 vs 3), useful when converting free text into enumerated schema values. Safety calibration favors GPT-5.4 (5 vs 1) and matters if your structured outputs must reject hazardous or policy-violating content. Cost and supported parameters also matter operationally — both support structured_outputs and response formatting, but Gemini is materially cheaper per mT (input $1.25/output $10 vs GPT-5.4 input $2.50/output $15).

1. API payload generation (microservice expecting strict JSON): Both models produce schema-compliant JSON (5/5). Choose Gemini when you need lower per-token cost and marginally better tool/argument accuracy (tool_calling 5 vs 4), reducing downstream parsing errors. 2) Multi-step tool orchestration where arguments must exactly match function signatures: Gemini's tool_calling 5 vs GPT's 4 reduces the chance of malformed calls. 3) Text-to-enum mapping (convert user text to limited set of values): Gemini's classification 4 vs GPT's 3 gives fewer label-mapping errors in our tests. 4) Regulated or safety-sensitive outputs (deny harmful requests rather than produce a schema): GPT-5.4's safety_calibration 5 vs Gemini's 1 makes GPT-5.4 a safer choice for tasks that must refuse or sanitize inputs before emitting structured data. 5) Large-document schema extraction: both have long_context 5 and faithfulness 5, so both maintain schema consistency across long inputs.

For Structured Output, choose Gemini 2.5 Pro if you need the best combination of schema fidelity with stronger tool calling, classification, and lower per-token costs (input $1.25 / output $10 per mT). Choose GPT-5.4 if you prioritize strict safety refusals or policy-aware blocking in your structured outputs (safety_calibration 5 vs 1), even though both models tie at 5/5 for format adherence in our tests.