Gemini 2.5 Pro vs GPT-5.4 for Agentic Planning
GPT-5.4 is the winner for Agentic Planning in our testing. On the agentic_planning test GPT-5.4 scores 5 vs Gemini 2.5 Pro's 4. That margin reflects GPT-5.4's stronger strategic_analysis (5 vs 4) and far higher safety_calibration (5 vs 1) in our benchmarks—both critical for robust goal decomposition and failure recovery. Gemini 2.5 Pro compensates with superior tool_calling (5 vs 4) and slightly better creative_problem_solving (5 vs 4), but those advantages do not outweigh GPT-5.4's lead on the planning-specific dimensions we measured.
Gemini 2.5 Pro
Benchmark Scores
External Benchmarks
Pricing
Input
$1.25/MTok
Output
$10.00/MTok
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openai
GPT-5.4
Benchmark Scores
External Benchmarks
Pricing
Input
$2.50/MTok
Output
$15.00/MTok
modelpicker.net
Task Analysis
Agentic Planning (goal decomposition and failure recovery) requires: precise strategic analysis to trade off options, reliable safety calibration to refuse or alter risky plans, structured_output for deterministic task steps, tool calling to sequence external actions, long_context to track multi-step state, and faithfulness to avoid hallucinated steps. In our data there is no external benchmark for this task, so the internal agentic_planning score is primary: GPT-5.4 = 5, Gemini 2.5 Pro = 4. Supporting metrics: GPT-5.4 leads on strategic_analysis (5 vs 4) and safety_calibration (5 vs 1), and ties on structured_output (both 5), faithfulness (both 5), and long_context (both 5). Gemini leads on tool_calling (5 vs 4) and creative_problem_solving (5 vs 4), which help when workflows require rich tool sequences or novel heuristics. These per-dimension scores explain why GPT-5.4 is better at robust, policy-safe planning in our tests, while Gemini is a strong, cheaper alternative for tool-heavy pipelines.
Practical Examples
- Safety-sensitive automation (win: GPT-5.4). Example: an AI assistant that must decompose a regulatory compliance goal and refuse or reroute unsafe tactics. GPT-5.4's safety_calibration 5 and strategic_analysis 5 give it the edge in our tests. 2) Complex multi-tool orchestration (win: Gemini 2.5 Pro). Example: sequencing API calls, updating trackers, and retrying failed steps—Gemini's tool_calling 5 helps ensure correct function selection and arguments. 3) Long, stateful project plans (tie on core needs). Both models score 5 on long_context and 5 on structured_output and faithfulness, so either can maintain 30K+ token state and produce compliant JSON step plans. 4) Cost-sensitive batch planning (win: Gemini 2.5 Pro for cost). Gemini input/output costs are lower (input $1.25/MTOK, output $10/MTOK) versus GPT-5.4 (input $2.50/MTOK, output $15/MTOK), making Gemini more economical for high-volume agent runs despite scoring 1 point lower on agentic_planning.
Bottom Line
For Agentic Planning, choose Gemini 2.5 Pro if you need higher tool_calling accuracy, better creative problem ideas, or lower per-token cost (input $1.25/MTOK, output $10/MTOK). Choose GPT-5.4 if you need the safest, most reliable goal decomposition and failure recovery from our tests—GPT-5.4 scores 5 vs Gemini's 4 on agentic_planning and outperforms Gemini on strategic_analysis (5 vs 4) and safety_calibration (5 vs 1).
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.