R1 0528 vs Gemini 3.1 Pro Preview

Summary of wins (our 12-test suite): each model wins 3 tests; 6 tests tie. R1 0528 wins tool calling (5 vs 4) — tied for 1st of 54 models in our ranking — so it is strongest when selecting functions, arguments, and sequencing. R1 also wins classification (4 vs 2), tied for 1st with 29 others out of 53, meaning it’s better at routing and categorization in our tests. R1 wins safety calibration (4 vs 2), ranking 6 of 55, so it refuses harmful requests more consistently in our runs. Gemini 3.1 Pro Preview wins structured output (5 vs 4), tied for 1st of 54, which directly translates to better JSON/schema compliance and format adherence for production pipelines. Gemini also wins strategic analysis (5 vs 4), tied for 1st, meaning more reliable nuanced tradeoff reasoning in numeric/financial/technical scenarios. Gemini’s creative problem solving score (5 vs 4) — tied for top — indicates stronger generation of non-obvious, feasible ideas. Ties: constrained rewriting (4/4), faithfulness (5/5), long context (5/5), persona consistency (5/5), agentic planning (5/5), multilingual (5/5) — both models perform equivalently on these tasks in our tests. External math benchmarks (Epoch AI): R1 scores 96.6% on MATH Level 5 (Epoch AI), while Gemini scores 95.6% on AIME 2025 (Epoch AI), indicating R1 is extremely strong on advanced math problems in our math tests while Gemini shines on the AIME external measure. Operational notes from the payload: R1’s quirks include returning empty responses on structured output, constrained rewriting, and agentic planning and it “uses_reasoning_tokens” and requires high max completion tokens — this affects short structured tasks and means reasoning tokens consume output budget on short calls. Gemini is multimodal (text+image+file+audio+video->text), supports a 1,048,576 token context window and max_output_tokens 65,536 — important for very long-context or multimodal workflows. Use the rankings: R1’s tool calling and classification wins are high-ranked (tied for 1st and tied for 1st respectively), while Gemini’s structured output and strategic analysis wins both sit at tied-for-1st positions, showing each model leads in different production-critical dimensions.

Pricing per mTok: R1 0528 = $0.50 input / $2.15 output; Gemini 3.1 Pro Preview = $2.00 input / $12.00 output. Assuming a 50/50 input/output token split: - 1M tokens (1,000 mTok): R1 ≈ $1,325; Gemini ≈ $7,000. - 10M tokens (10,000 mTok): R1 ≈ $13,250; Gemini ≈ $70,000. - 100M tokens (100,000 mTok): R1 ≈ $132,500; Gemini ≈ $700,000. That matches a priceRatio ≈ 0.179 (R1 costs ~17.9% of Gemini for equivalent token mix). Who should care: startups, high-volume API customers, or any team running >10M tokens/month — R1 delivers large dollar savings. Teams that need best-in-class structured output, multimodal workflows, or heavy long-run strategic analysis may justify Gemini’s ~5–6x higher bill.

Choose R1 0528 if: - You are cost-sensitive or run high-volume APIs (R1 input $0.50 / output $2.15). - Your workload prioritizes tool calling, classification, or stricter safety calibration (R1 wins these tests and ranks top in tool calling and classification). - You can accommodate R1’s quirks (it may return empty on structured output and needs large max completion tokens). Choose Gemini 3.1 Pro Preview if: - You need robust structured output (JSON/schema), top-tier strategic analysis, creative problem solving, or long multimodal contexts (Gemini wins structured output, strategic analysis, creative problem solving and supports text+image+file+audio+video->text and a 1,048,576 token window). - You can absorb the higher cost ($2/$12 per mTok) for better out-of-the-box structured/strategic behavior. If you need both, prototype on R1 for cost and switch to Gemini for mission-critical structured/strategic paths.