Gemini 3.1 Pro Preview vs Gemma 4 31B

Across our 12-task suite the two models tie on 8 tasks, each wins 2 tasks. In our testing: Gemini 3.1 Pro Preview wins creative_problem_solving (5 vs 4) and long_context (5 vs 4). Gemini ranks tied for 1st on long_context (tied with 36 others out of 55) and tied for 1st on creative_problem_solving (tied for 1st with 7 others out of 54), and it posts 95.6% on AIME 2025 (Epoch AI), ranking 2nd of 23 on that external math benchmark. Gemma 4 31B wins tool_calling (5 vs 4) and classification (4 vs 2). Gemma ranks tied for 1st on tool_calling (tied with 16 others of 54) and tied for 1st on classification (tied with 29 others of 53), making it the stronger choice where function selection, argument accuracy, sequencing, and routing are primary. They tie at top scores on structured_output, strategic_analysis, constrained_rewriting, faithfulness, safety_calibration, persona_consistency, agentic_planning, and multilingual tasks. Practical meaning: Gemini’s advantages translate to better retrieval and reasoning over 30K+ token contexts and superior creative / mathematical outputs in our tests; Gemma’s wins mean more dependable tool integration and significantly stronger classification/routing behavior at much lower cost.

Pricing (input+output per 1,000-token unit from the payload): Gemini 3.1 Pro Preview = $2 (input) + $12 (output) = $14 per m-token; Gemma 4 31B = $0.13 + $0.38 = $0.51 per m-token. At scale this produces large gaps: per 1M tokens/month (1,000 m-tokens) Gemini ≈ $14,000 vs Gemma ≈ $510. At 10M tokens: Gemini ≈ $140,000 vs Gemma ≈ $5,100. At 100M tokens: Gemini ≈ $1,400,000 vs Gemma ≈ $51,000. The payload also gives an output-cost ratio of 31.5789 (Gemini $12 vs Gemma $0.38 per m-token). Who should care: teams with heavy inference volume (≥10M tokens/month), consumer-facing apps, or cost-constrained deployments should prefer Gemma 4 31B; research or high-value workflows requiring long-context reasoning, creative problem solving, or top math performance may justify Gemini’s much higher expense.

Choose Gemini 3.1 Pro Preview if you need superior long-context retrieval, creative problem solving, or high-stakes math/reasoning (AIME 95.6% in Epoch AI) and can absorb far higher inference costs. Choose Gemma 4 31B if you prioritize tool calling, classification, and cost-efficiency for high-volume production (Gemma output $0.38 vs Gemini $12 per m-token), or when every dollar per million tokens matters.