R1 0528 vs GPT-5.4 for Tool Calling

Tool Calling demands accurate function selection, precise argument construction, correct sequencing of calls, and predictable structured outputs. Key capabilities: tool_choice/tools support, structured_output adherence, agentic_planning for call sequencing, faithfulness to avoid hallucinated arguments, long_context when call histories or tool docs are large, and safety_calibration to refuse dangerous actions. On our Task: R1 0528 achieved 5/5 for tool_calling (ranked tied for 1st with other top models), while GPT-5.4 achieved 4/5 (rank 18). Supporting signals: R1 also scores 5 on agentic_planning and 4 on structured_output, indicating strong sequencing and good but imperfect schema compliance; GPT-5.4 scores 5 on structured_output and 5 on agentic_planning, indicating better JSON/schema adherence and planning but slightly weaker function selection in our tests. Additional context: R1 uses reasoning tokens and has a quirk where it returns empty responses on some structured_output and agentic tests (note this can consume output budget on short tasks). GPT-5.4 provides broader modality support and a far larger context window (1,050,000 tokens) and higher safety_calibration, which matters for multi-step, safety-sensitive tool flows. Where present, external benchmarks are supplementary: GPT-5.4 scores 76.9% on SWE-bench Verified (Epoch AI), while R1 shows high math scores (MATH Level 5 96.6%, AIME 2025 66.4% — Epoch AI). Those external figures are informative for code/math tasks but do not override our internal tool_calling result.

Where R1 0528 shines (choose R1):

• High-throughput automation: frequent, small function calls where precise function selection and argument accuracy matter — R1 scored 5 vs GPT-5.4's 4 for Tool Calling and is far cheaper at $2.15 vs $15 per output mTok.
• Multi-step agentic workflows where correct sequencing is critical: R1 scored 5 on agentic_planning, supporting reliable decomposition and call ordering.
• Cost-constrained SaaS pipelines: output token cost favors R1 (~$2.15) for volume-driven tool use. Where GPT-5.4 shines (choose GPT-5.4):
• Strict JSON/schema enforcement: GPT-5.4 scores 5 on structured_output vs R1's 4, so it better adheres to schemas and avoids format rework.
• Safety-sensitive integrations: GPT-5.4 scored 5 on safety_calibration while R1 scored 4, useful when tool calls must guard against harmful actions.
• Very long context or multimodal tool flows: GPT-5.4 supports a 1,050,000-token window and multimodal inputs, which helps when tool selection depends on long histories or files. Concrete numeric examples from our tests: R1 tool_calling 5 vs GPT-5.4 4; structured_output 4 (R1) vs 5 (GPT-5.4); agentic_planning both 5. Cost per output mTok: $2.15 (R1) vs $15 (GPT-5.4).

For Tool Calling, choose R1 0528 if you need best-in-class function selection and sequencing at lower cost (R1: 5/5 tool_calling, rank 1, $2.15/output mTok). Choose GPT-5.4 if your priority is strict JSON/schema compliance, stronger safety calibration, or massive context/multimodal inputs (GPT-5.4: structured_output 5, safety_calibration 5, 1,050,000-token window), accepting a higher cost ($15/output mTok).