Gemini 2.5 Pro vs GPT-5

Overview (our 12-test suite + external measures): GPT-5 wins 4 benchmarks (strategic analysis, constrained rewriting, safety calibration, agentic planning), Gemini 2.5 Pro wins 1 (creative problem solving), and they tie on 7 tests (structured output, tool calling, faithfulness, classification, long context, persona consistency, multilingual). Key task-level highlights:

• Math & coding (external benchmarks): GPT-5 scores 73.6% on SWE-bench Verified (Epoch AI) vs Gemini's 57.6% — a material gap for real GitHub issue/code tasks. GPT-5 also scores 98.1% on Math Level 5 (Epoch AI) and 91.4% on AIME 2025, while Gemini posts 84.2% on AIME 2025. These external metrics support GPT-5 for high-end math and coding.
• Strategic analysis and agentic planning: GPT-5 scores 5 vs Gemini's 4 on strategic analysis and agentic planning; GPT-5 ranks tied for 1st for strategic analysis and tied for 1st for agentic planning, while Gemini ranks lower (strategic analysis rank 27/54, agentic planning rank 16/54). Expect GPT-5 to produce stronger nuanced tradeoffs and goal decomposition in our tests.
• Creative problem solving: Gemini 2.5 Pro scores 5 vs GPT-5's 4 and ranks tied for 1st on creative problem solving — this indicates Gemini is more likely to produce non-obvious, specific, feasible ideas in our benchmarks.
• Structured output, tool calling, faithfulness, classification, long context, persona, multilingual: both score 5 and tie, and both are tied for top ranks in long context, structured output, tool calling, faithfulness and multilingual. Practically, both handle JSON/schema outputs, function selection, 30K+ retrieval tasks, and non-English output reliably in our suite.
• Constrained rewriting & safety: GPT-5 wins constrained rewriting (4 vs 3) and has higher safety calibration (2 vs 1). GPT-5's safety calibration rank is 12/55 vs Gemini's 32/55, meaning GPT-5 more consistently refuses harmful requests or permits legitimate ones per our test set. In short: GPT-5 leads for math, coding, strategic tasks and safety calibration in our testing; Gemini leads creative ideation and brings a much larger raw context window and more media modalities (per the payload).

Both models share identical pricing in the payload: input $1.25 per mTok and output $10.00 per mTok. Using a 50/50 input-output token split as an example: at 1M tokens/month (500 mTok input + 500 mTok output) cost = $625 + $5,000 = $5,625/month. At 10M tokens/month cost = $6,250 + $50,000 = $56,250/month. At 100M tokens/month cost = $62,500 + $500,000 = $562,500/month. Because output tokens are far more expensive ($10/mTok), workloads that generate large outputs (document generation, long-form summaries, batch API responses) drive costs; teams focused on lots of short replies or mostly inputs should still watch output volume. Since price parity is exact here, choose on capability (model scores, context window, modality) rather than cost differences.

Choose Gemini 2.5 Pro if: you need the largest context window (1,048,576 tokens), multimodal inputs including audio/video, or you prioritize creative, non-obvious idea generation (Gemini wins creative problem solving). Choose GPT-5 if: you need top math/coding performance (SWE-bench 73.6% and Math Level 5 98.1%), stronger strategic analysis and agentic planning, or better constrained rewriting and safety calibration. Because both models have identical input/output pricing in the payload, choose on capability fit and external benchmark performance rather than cost.