Gemini 2.5 Pro vs GPT-5.4 for Tool Calling

What Tool Calling demands: selecting the right function, producing correct and complete arguments, ordering calls when multiple tools are required, and returning structured, schema-compliant outputs for programmatic consumption. Key capabilities that matter: (1) tool_choice and tools parameter support and correctness, (2) structured_output / response_format adherence, (3) argument accuracy (types, units, required keys), (4) sequencing and dependency handling across multi-step calls, and (5) predictable refusal/safety behavior when calls would be harmful. In our testing, Gemini 2.5 Pro earned a 5/5 on tool_calling while GPT-5.4 earned 4/5; Gemini also scores 5/5 on structured_output and faithfulness, supporting its ability to produce schema-compliant, accurate arguments. GPT-5.4 ties on structured_output (5/5) but scores lower on tool_calling (4/5) while scoring higher on agentic_planning (5/5) and safety_calibration (5/5). Both models expose relevant parameters (tools, tool_choice, structured_outputs) in their supported_parameters lists; Gemini additionally advertises reasoning tokens usage, which in our testing appears to help with argument precision and sequencing.

Where Gemini 2.5 Pro shines (based on score gap):

• Multi-API orchestration: For a request that requires picking two APIs in sequence (fetch user data → call personalization API → post result), Gemini's 5/5 tool_calling and 5/5 structured_output produced correct function selection and exact argument schemas more reliably than GPT-5.4 (5 vs 4).
• Argument accuracy: When a tool expected nested JSON (ids, timestamps, flags), Gemini produced exact keys and types; GPT-5.4 more frequently required a corrective prompt to fix missing fields. This mirrors the 5 vs 4 tool_calling scores.
• Cost-sensitive production: Gemini is cheaper per token (input $1.25/mTok, output $10/mTok) than GPT-5.4 (input $2.50/mTok, output $15/mTok), so at scale Gemini reduces tool-calling runtime cost while improving correctness. Where GPT-5.4 is preferable:
• Complex planning with recovery: GPT-5.4 scores 5/5 on agentic_planning and 5/5 on safety_calibration, so for workflows that demand sophisticated goal decomposition, backtracking, or strict refusal behavior before making potentially harmful tool calls, GPT-5.4's strengths reduce risky or ill-considered call sequences.
• Safety-critical gating: If your tool-calling flow must enforce strong safety checks before executing external actions, GPT-5.4's higher safety_calibration (5/5 vs Gemini's 1/5 in our tests) is a practical advantage.

For Tool Calling, choose Gemini 2.5 Pro if you need the most accurate function selection, argument correctness, and sequencing at lower per-token cost (Gemini: tool_calling 5/5, rank 1; input $1.25/mTok, output $10/mTok). Choose GPT-5.4 if your workflow requires stronger agentic planning or strict safety gating around calls—GPT-5.4 scores 5/5 on agentic_planning and safety_calibration but scores 4/5 on tool_calling and is costlier (input $2.50/mTok, output $15/mTok).