R1 vs Gemma 4 31B

Summary (all comparisons are in our testing):

• Gemma 4 31B wins: structured_output 5 vs R1 4 (Gemma tied for 1st of 54), tool_calling 5 vs R1 4 (Gemma tied for 1st of 54), classification 4 vs R1 2 (Gemma tied for 1st of 53; R1 rank 51 of 53), safety_calibration 2 vs R1 1 (Gemma rank 12 of 55), agentic_planning 5 vs R1 4 (Gemma tied for 1st of 54). These wins indicate Gemma is measurably better at function selection/argument accuracy, strict JSON/schema outputs, routing/classification, safe refusals, and goal decomposition in agentic flows.
• R1 wins: creative_problem_solving 5 vs Gemma 4 (R1 tied for 1st of 54; Gemma rank 9). This reflects R1's edge on producing non-obvious, specific feasible ideas in our tests.
• Ties: strategic_analysis (both 5, tied for 1st), constrained_rewriting (4), faithfulness (5), long_context (4), persona_consistency (5), multilingual (5). Ties mean both models perform comparably on nuanced tradeoff reasoning, constrained rewriting, sticking to source material, long-context retrieval, persona stability, and multilingual output in our suite.
• External math benchmarks (Epoch AI): R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025; these are third-party measures and show R1's strength on high-level math problems but middling AIME performance relative to that specific set (R1 ranks 8 of 14 on MATH Level 5 and 17 of 23 on AIME in the payload). Gemma 4 31B has no external math scores in the payload. What this means for real tasks: choose Gemma when you need robust tool integrations, strict schema outputs, classification, agentic orchestration, or a much lower token bill. Choose R1 when you need top-tier creative ideation or strong MATH Level 5 performance and are willing to pay a material premium.

Raw token costs from the payload: R1 input $0.70 /M-token and output $2.50 /M-token; Gemma 4 31B input $0.13 /M-token and output $0.38 /M-token. If you assume 1:1 input:output token usage, combined per-million costs are $3.20 for R1 and $0.51 for Gemma. At scale that yields: 1M tokens → R1 $3.20 vs Gemma $0.51; 10M → R1 $32.00 vs Gemma $5.10; 100M → R1 $320.00 vs Gemma $51.00. The payload also reports an output cost ratio of 6.5789 (R1 output $2.50 ÷ Gemma output $0.38). Who should care: high-volume production apps, consumer-facing chatbots, or any service with millions of tokens/month should prefer Gemma to reduce variable costs; teams focused on niche creative or advanced-math research may justify R1's premium but must budget ~6.6× higher output costs.

Choose Gemma 4 31B if you need: production-ready agents, reliable function/tool calling, precise JSON or schema outputs, classification, multimodal inputs (text+image+video→text), or low token costs (input $0.13 /M, output $0.38 /M). Choose R1 if you need: superior creative problem solving (5/5 in our tests), strong MATH Level 5 results (93.1% on Epoch AI), or specific research use cases where those strengths justify ~6.6× higher per-output-token cost. If budget and high throughput matter, pick Gemma; if a single feature (creative/math) is mission-critical and budget is secondary, pick R1.