R1 0528 vs GPT-5.4 for Agentic Planning

Agentic Planning demands: clear goal decomposition, robust failure detection and recovery, correct tool selection and sequencing, machine-readable structured plans, long-context awareness, faithfulness to inputs, and safe refusal when tasks are harmful. Across these axes in our testing: both models score 5/5 on the agentic_planning test, and both have top-tier long_context (5) and faithfulness (5). Key differentiators: tool_calling (R1 0528 = 5, GPT-5.4 = 4) matters for accurate function selection and sequencing; structured_output (R1 4, GPT-5.4 5) matters for JSON schemas, API args, and deterministic orchestration; safety_calibration (R1 4, GPT-5.4 5) matters when plans could touch restricted content or risky actions. Also consider engineering constraints: GPT-5.4’s 1,050,000 token context and explicit large max_output_tokens (128,000) support very long plans and checkpoints, while R1 0528 has a 163,840 token window and a documented quirk — it can return empty responses on structured_output and agentic_planning and requires high max completion tokens. Use the tool_calling and structured_output scores together: R1 is stronger at tool selection, GPT-5.4 is stronger at producing reliable, schema-compliant plan outputs and safer refusals. All benchmark claims here are from our testing.

1. Large orchestration with long state: Building a multi-day agent that ingests 500k tokens of logs and outputs a stepwise recovery plan — GPT-5.4 is preferable because of its 1,050,000 token context and structured_output 5, reducing the chance of truncated or malformed JSON plans. 2) Cost-sensitive developer agent that composes many short tool calls: R1 0528 shines when tool selection and sequencing are paramount (tool_calling 5 vs GPT-5.4 4) and budget matters — output cost per mTok is $2.15 for R1 0528 vs $15 for GPT-5.4. Example cost: a 10k-token plan (≈10 mTok) costs ≈$21.50 on R1 0528 vs ≈$150 on GPT-5.4. 3) Schema-driven automation for production APIs: GPT-5.4’s structured_output 5 and safety 5 reduce integration failures and unsafe plan generation; R1’s documented quirk (empty on structured_output/agentic_planning) makes it risky unless you can guarantee large completion tokens and custom prompts to avoid empty outputs. 4) Rapid prototyping with many function calls: R1 0528 can iterate cheaper and may return better tool-choice sequences, but expect extra engineering to work around its empty-response behavior in structured outputs.

For Agentic Planning, choose R1 0528 if you need lower-cost inference and the best tool_calling behavior (tool_calling 5) and you can accommodate its quirks (requires high max completion tokens and may return empty structured outputs). Choose GPT-5.4 if you need production reliability: stronger structured_output (5 vs 4), safer refusal behavior (5 vs 4), a much larger context window (1,050,000 vs 163,840), and no empty-response quirk — at a higher cost (output $15 vs $2.15 per mTok). Both score 5/5 on agentic_planning in our testing; pick by reliability and cost tradeoffs.