R1 vs Gemini 3.1 Pro Preview
Gemini 3.1 Pro Preview outperforms R1 on structured output, long context, agentic planning, and safety calibration in our testing — making it the stronger choice for agentic and document-heavy workflows. R1 ties on eight other benchmarks while costing roughly 80% less on output tokens ($2.50/M vs $12.00/M), so the gap in capability rarely justifies the gap in price for general use. For math-intensive tasks, the AIME 2025 external benchmark tells a clear story: Gemini 3.1 Pro Preview scores 95.6% (rank 2 of 23) vs R1's 53.3% (rank 17 of 23), according to Epoch AI — if advanced math is your core workload, Gemini 3.1 Pro Preview is the decisive winner.
deepseek
R1
Benchmark Scores
External Benchmarks
Pricing
Input
$0.700/MTok
Output
$2.50/MTok
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Gemini 3.1 Pro Preview
Benchmark Scores
External Benchmarks
Pricing
Input
$2.00/MTok
Output
$12.00/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test internal suite, R1 wins zero benchmarks outright and ties eight with Gemini 3.1 Pro Preview. Gemini 3.1 Pro Preview wins four.
Where Gemini 3.1 Pro Preview wins:
- Structured output (5 vs 4): Gemini 3.1 Pro Preview scores 5/5 (tied for 1st among 54 models); R1 scores 4/5 (rank 26 of 54). For JSON schema compliance and format-strict APIs, this difference is operationally significant.
- Long context (5 vs 4): Gemini 3.1 Pro Preview scores 5/5 (tied for 1st among 55 models); R1 scores 4/5 (rank 38 of 55). Gemini 3.1 Pro Preview also carries a 1,048,576-token context window vs R1's 64,000 — over 16x larger. For retrieval across large codebases or documents, this is a hard capability gap, not just a score gap.
- Agentic planning (5 vs 4): Gemini 3.1 Pro Preview scores 5/5 (tied for 1st among 54 models); R1 scores 4/5 (rank 16 of 54). Better goal decomposition and failure recovery matters for multi-step autonomous workflows.
- Safety calibration (2 vs 1): Gemini 3.1 Pro Preview scores 2/5 (rank 12 of 55); R1 scores 1/5 (rank 32 of 55). Both sit below the median (p50 = 2), but R1's score places it near the bottom of the field. Neither model should be deployed in safety-critical contexts without guardrails, but R1 requires more attention here.
Where they tie (8 benchmarks): Both models score 5/5 on multilingual, persona consistency, strategic analysis, faithfulness, and creative problem solving — all at or near the top of our rankings. Both score 4/5 on tool calling, constrained rewriting. Both score 2/5 on classification (rank 51 of 53 — a shared weakness worth noting for routing and categorization tasks).
External benchmarks (Epoch AI): On AIME 2025 (math olympiad), Gemini 3.1 Pro Preview scores 95.6% (rank 2 of 23 models) vs R1's 53.3% (rank 17 of 23) — a 42-point gap that makes Gemini 3.1 Pro Preview the clear choice for advanced mathematical reasoning. On MATH Level 5 (competition math), R1 scores 93.1% (rank 8 of 14 models with data); Gemini 3.1 Pro Preview has no MATH Level 5 score in our payload. These are external benchmarks from Epoch AI, not our internal testing.
Pricing Analysis
R1 costs $0.70/M input and $2.50/M output. Gemini 3.1 Pro Preview costs $2.00/M input and $12.00/M output — 2.9x more on input and 4.8x more on output. At real-world volumes, that gap compounds fast. At 1M output tokens/month: R1 costs $2.50 vs $12.00 for Gemini 3.1 Pro Preview — a $9.50 difference you might not notice. At 10M output tokens/month: $25 vs $120 — $95/month, meaningful for small teams. At 100M output tokens/month: $250 vs $1,200 — a $950/month gap that demands justification. Given that R1 ties Gemini 3.1 Pro Preview on eight of twelve internal benchmarks, the premium is hard to justify unless you specifically need Gemini 3.1 Pro Preview's wins in structured output, long context, agentic planning, or safety calibration — or the dramatically better AIME 2025 math performance. Developers running high-volume, general-purpose inference should default to R1. Teams building document pipelines, long-context retrieval, or multi-step agents over 30K+ tokens have a concrete reason to pay for Gemini 3.1 Pro Preview.
Real-World Cost Comparison
Bottom Line
Choose R1 if: You need strong general-purpose reasoning at low cost. R1's $2.50/M output price makes it viable at high volume, and it ties Gemini 3.1 Pro Preview on eight of twelve benchmarks — including multilingual, faithfulness, strategic analysis, and creative problem solving. It's the right call for most API integrations, content pipelines, and chat applications where you're not pushing past 64K context or running complex multi-step agents. Also consider R1 if MATH Level 5 is relevant — it holds a 93.1% score on that external benchmark (Epoch AI).
Choose Gemini 3.1 Pro Preview if: Your workload involves long documents (over 64K tokens), structured data extraction requiring strict JSON compliance, multi-step agentic pipelines, or advanced math reasoning. The 1M+ token context window is a hard R1 blocker for large-document use cases. Gemini 3.1 Pro Preview's 95.6% AIME 2025 score (Epoch AI, rank 2 of 23) makes it the top-tier choice for mathematical reasoning applications. The 4.8x output cost premium is defensible if these specific capabilities drive your use case — but not as a general upgrade from R1.
How We Test
We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.