R1 vs Gemini 3.1 Flash Lite Preview

We ran our 12-test suite and compare each test below (scores shown are from our testing): - Structured output: Gemini 3.1 Flash Lite Preview wins (5 vs R1's 4). Gemini ranks tied for 1st on structured_output (rank 1 of 54) vs R1's rank 26 of 54, so Gemini is the safer pick for JSON/schema compliance and strict format adherence. - Classification: Gemini wins (3 vs R1's 2). R1 ranks poorly on classification (rank 51 of 53) while Gemini is mid-pack (rank 31 of 53), so routing/labeling tasks favor Gemini. - Safety calibration: Gemini wins decisively (5 vs R1's 1). Gemini is tied for 1st on safety_calibration, R1 sits at rank 32 of 55 — critical if you need reliable refusals and correct permissions. - Creative problem solving: R1 wins (5 vs Gemini's 4). R1 is tied for 1st on creative_problem_solving, indicating stronger non-obvious, specific, feasible ideas in our tests. - Ties (no clear winner): strategic_analysis (5/5 both), constrained_rewriting (4/4 both), tool_calling (4/4 both), faithfulness (5/5 both), long_context (4/4 both), persona_consistency (5/5 both), agentic_planning (4/4 both), multilingual (5/5 both). For these tied tasks, ranks show both models performing at or near top tiers — e.g., both tie for 1st on strategic_analysis and faithfulness. Additional context from external benchmarks: R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI), which supplements our internal results and explains R1's edge in math/structured reasoning tasks; Gemini has no external MATH/AIME scores included in the payload. Practical implications: pick Gemini for classification, schema outputs, and safety-critical flows; pick R1 when you need the best creative problem solving and stronger external math performance. Also note platform capabilities in the payload: R1 has a 64k context window and uses explicit reasoning tokens (quirk: requires high max_completion_tokens, min 1,000), while Gemini offers a much larger 1,048,576 token context window, multimodal input (text+image+file+audio+video->text), and a larger max output (65,536 tokens).

Prices in the payload are per mTok (per 1,000 tokens). If you assume total tokens are split 50/50 input/output: - Per 1M tokens (500k input + 500k output): R1 = (500 * $0.70) + (500 * $2.50) = $350 + $1,250 = $1,600. Gemini = (500 * $0.25) + (500 * $1.50) = $125 + $750 = $875. - Per 10M tokens: R1 = $16,000; Gemini = $8,750. - Per 100M tokens: R1 = $160,000; Gemini = $87,500. R1 is ~1.83x more expensive at these mixed usage assumptions due to its high output price ($2.50/1k). Who should care: large-scale chat or analytics products with heavy output (long answers, many completions) will see the biggest delta; small-scale or research users less so. If your workload is input-heavy (many short prompts), the gap narrows but Gemini remains materially cheaper ($0.25 vs $0.70 per 1k input).

Choose R1 if: - You need top-tier creative problem solving (R1 score 5) or strong external math performance (93.1% on MATH Level 5, 53.3% on AIME 2025, Epoch AI). - You accept higher per-token costs and can handle R1’s quirks (reasoning tokens, min completion size). Choose Gemini 3.1 Flash Lite Preview if: - You operate at scale or have many output tokens — Gemini is materially cheaper ($0.25/$1.50 vs $0.70/$2.50) and halves monthly bill in many scenarios. - You require safer refusals and structured output (safety 5, structured_output 5), better classification, multimodal inputs, or massive context windows (1,048,576 tokens).