Gemini 3.1 Pro Preview vs o3

Across our 12-test suite, Gemini 3.1 Pro Preview wins 3 tests, o3 wins 2, and the rest tie. Where Gemini wins: creative_problem_solving 5 vs 4 (Gemini tied for 1st among 54 models, o3 rank 9 of 54), long_context 5 vs 4 (Gemini tied for 1st of 55; o3 rank 38 of 55) and safety_calibration 2 vs 1 (Gemini rank 12 of 55; o3 rank 32). These indicate Gemini is measurably better for non-obvious idea generation, and handling very long documents/30K+ contexts with higher retrieval fidelity, and it more often refuses or correctly frames borderline requests. Where o3 wins: tool_calling 5 vs 4 (o3 tied for 1st of 54; Gemini rank 18) and classification 3 vs 2 (o3 rank 31; Gemini rank 51). That means o3 is stronger at function selection, argument correctness and routing/tagging tasks. Ties (both at 5 in many cases) include structured_output, strategic_analysis, constrained_rewriting, faithfulness, persona_consistency, agentic_planning and multilingual — both models are top performers there (structured_output tied for 1st of 54). External benchmarks (Epoch AI): o3 scores 97.8% on MATH Level 5 and 62.3% on SWE-bench Verified, while Gemini scores 95.6% on AIME 2025 — we cite these Epoch AI results as supplementary data points. In practice: pick Gemini for high-fidelity long-context workflows and creative technical tasks; pick o3 for robust tool-calling, classification, math-level tasks (MATH Level 5), and materially lower output spend.

Output cost per 1K tokens: Gemini 3.1 Pro Preview $12, o3 $8; input cost per 1K tokens: both $2. Output-only cost at scale: 1M tokens → Gemini $12,000 vs o3 $8,000; 10M → Gemini $120,000 vs o3 $80,000; 100M → Gemini $1,200,000 vs o3 $800,000. Including input billing (both $2/1K) adds $2,000 per 1M tokens: combined per 1M = Gemini $14,000 vs o3 $10,000 (10M → $140k vs $100k; 100M → $1.4M vs $1.0M). Teams doing low-volume prototypes won’t feel the gap; production deployments at 10M–100M tokens/month should budget the 1.4x output-cost gap. High-throughput platforms, API-first startups, and apps with long-lived chat histories should care most about the delta.

Choose Gemini 3.1 Pro Preview if you need top-tier long-context handling (context window 1,048,576 tokens), better creative problem-solving (5 vs 4), and slightly stronger safety calibration — suitable for research, large-document analysis, and multimodal, high-quality outputs despite higher per-token cost. Choose o3 if you need the best tool-calling and classification behavior (tool_calling 5 vs 4; classification 3 vs 2), strong math performance on MATH Level 5 (97.8% per Epoch AI), and lower output costs ($8 vs $12 per 1K) for production-scale APIs and function-driven agent pipelines.