Gemini 3.1 Pro Preview vs GPT-5 Nano

Summary (our 12-test suite): Gemini 3.1 Pro Preview wins 6 categories, GPT-5 Nano wins 2, and 4 are ties. In our testing: - Gemini wins strategic_analysis (5 vs 4). Rank: Gemini is tied for 1st in strategic analysis (tied with 25 others out of 54). Practical effect: better at nuanced trade-off reasoning and numeric tradeoffs. - Gemini wins constrained_rewriting (4 vs 3). Rank: Gemini ranks 6 of 53. Practical effect: better at compressing content under strict limits. - Gemini wins creative_problem_solving (5 vs 3). Rank: tied for 1st in creative problem solving. Practical effect: generates more specific, feasible ideas for product/design tasks. - Gemini wins faithfulness (5 vs 4). Rank: tied for 1st in faithfulness. Practical effect: sticks to source material and reduces hallucinations in knowledge-sensitive outputs. - Gemini wins persona_consistency (5 vs 4) and agentic_planning (5 vs 4). Ranks: tied for 1st for persona consistency and agentic planning. Practical effect: superior character/state maintenance and goal decomposition for agentic workflows. - GPT-5 Nano wins classification (3 vs 2). Rank: GPT-5 Nano ranks 31 of 53 vs Gemini at 51 of 53; practical effect: GPT-5 Nano is better for routing, tagging, and categorical decisions. - GPT-5 Nano also wins safety_calibration (4 vs 2). Rank: GPT-5 Nano is rank 6 of 55 vs Gemini rank 12 of 55. Practical effect: GPT-5 Nano better balances refusal/allow decisions for borderline requests. - Ties: structured_output (5/5), tool_calling (4/4), long_context (5/5), and multilingual (5/5) — both models match at top-tier levels here. Ranks: both tied for 1st in structured_output and long_context; tool_calling ranks 18/54 for both. External benchmarks (Epoch AI): Gemini scores 95.6% on AIME 2025 (Epoch AI), ranking 2 of 23; GPT-5 Nano scores 95.2% on MATH Level 5 (Epoch AI) and 81.1% on AIME 2025, ranking 7/14 on MATH Level 5 and 14/23 on AIME. These external results reinforce Gemini’s edge on harder competition-style math/analytic tasks in our comparisons. Overall interpretation: Gemini is stronger where deep reasoning, creativity, faithfulness, and agentic planning matter; GPT-5 Nano is stronger/safer for classification and refusal behavior and wins on cost and latency.

Payload prices are per mTok (per 1k tokens). Gemini 3.1 Pro Preview costs $2 input / $12 output per mTok; GPT-5 Nano costs $0.05 input / $0.40 output per mTok — roughly a 30× price gap (priceRatio: 30). Assuming a 50/50 split of tokens between input and output: for 1M tokens/month (1,000 mTok) Gemini = $7,000 (500 mTok input × $2 = $1,000; 500 mTok output × $12 = $6,000). GPT-5 Nano = $225 (500 × $0.05 = $25; 500 × $0.4 = $200). At 10M tokens/month Gemini ≈ $70,000 vs GPT-5 Nano ≈ $2,250. At 100M tokens/month Gemini ≈ $700,000 vs GPT-5 Nano ≈ $22,500. Who should care: any product with >1M tokens/month should model costs — Gemini is a premium, high-cost choice for mission-critical reasoning and creativity; GPT-5 Nano is the economical option for high-volume, latency-sensitive, or cost-constrained deployments.

Choose Gemini 3.1 Pro Preview if your product prioritizes high-fidelity reasoning, creative problem solving, agentic planning, large-context multimodal workflows, and you can absorb the significant cost (e.g., $7k/month at 1M tokens with a 50/50 split). Choose GPT-5 Nano if you need ultra-low-cost, high-throughput inference with solid safety calibration and classification (≈ $225/month at 1M tokens with a 50/50 split), or if latency and cost-per-call are the decisive constraints.