Gemini 2.5 Flash vs GPT-5.2

All benchmark claims below are based on our internal 12-test suite (scores 1–5) unless noted otherwise. Overall outcome: GPT-5.2 wins 6 categories, Gemini 2.5 Flash wins 1, and 5 are ties. Key wins for GPT-5.2 (score 5 vs Gemini's lower score): strategic_analysis (5 vs 3) — GPT-5.2 ranks tied for 1st in strategic analysis in our tests ("Nuanced tradeoff reasoning"); creative_problem_solving (5 vs 4) — GPT-5.2 tied for 1st; faithfulness (5 vs 4) — GPT-5.2 tied for 1st, indicating stronger adherence to source material; classification (4 vs 3) — GPT-5.2 tied for 1st; safety_calibration (5 vs 4) — GPT-5.2 tied for 1st, meaning better refusal/allow behavior; agentic_planning (5 vs 4) — GPT-5.2 tied for 1st for goal decomposition and recovery. Gemini's clear win is tool_calling (5 vs 4): Gemini is tied for 1st on tool calling in our tests (rank display: "tied for 1st with 16 other models"), showing stronger function selection and argument accuracy in our scenarios; GPT-5.2's tool_calling ranks 18 of 54. Five categories tie: structured_output (4/4, both rank 26 of 54), constrained_rewriting (4/4, both rank 6 of 53), long_context (5/5, both tied for 1st), persona_consistency (5/5, both tied for 1st), and multilingual (5/5, both tied for 1st) — these ties mean both models perform comparably on JSON/schema compliance, tight compression, retrieval at 30K+ tokens, persona maintenance, and non-English outputs in our tests. External benchmarks: GPT-5.2 scores 73.8% on SWE-bench Verified (Epoch AI), ranking 5 of 12, and 96.1% on AIME 2025 (Epoch AI), ranking 1 of 23 — we report those external numbers as supplementary evidence (Epoch AI). Gemini has no SWE-bench/AIME external scores in the payload. Operational differences from the payload: Gemini has a much larger context_window (1,048,576 tokens) vs GPT-5.2 (400,000 tokens) and broader input modalities (Gemini accepts audio and video inputs -> text in the payload), which matter for long-document and multimodal ingestion use cases.

Pricing in the payload is per million tokens: Gemini 2.5 Flash input $0.30/M + output $2.50/M; GPT-5.2 input $1.75/M + output $14.00/M. Using a simple 50/50 input/output split as an illustrative example, Gemini costs ~$1.40 per 1M total tokens while GPT-5.2 costs ~$7.88 per 1M total tokens. At scale that gap widens linearly: for 1M/10M/100M tokens (50/50) Gemini ≈ $1.40 / $14.00 / $140.00 while GPT-5.2 ≈ $7.88 / $78.75 / $787.50. For output-heavy workloads (90% output), Gemini ≈ $2.28/M vs GPT-5.2 ≈ $12.78/M, so the output-rate dominates costs and GPT-5.2 becomes ~5–6x more expensive. Who should care: SaaS providers, high-volume chat or content generation services, and any organization with 10M+ tokens/month — the cost delta becomes material to unit economics and pricing strategies.

Choose Gemini 2.5 Flash if: you need best-in-class tool-calling, massive context windows (1,048,576 tokens), broader multimodal input (audio/video), or you must minimize cost — Gemini’s input/output pricing ( $0.30/M and $2.50/M ) makes it far cheaper at scale. Choose GPT-5.2 if: you need top performance on strategic analysis, faithfulness, safety calibration, agentic planning, classification, or creative problem solving — GPT-5.2 wins 6 of 12 benchmarks in our testing and posts strong external math/coding results (73.8% on SWE-bench Verified and 96.1% on AIME 2025, Epoch AI). If budget is tight and your workload is tool-heavy or multimodal, pick Gemini; if accuracy, safety, and highest analytic quality are core to your product, pick GPT-5.2 despite the higher cost.