Gemini 2.5 Pro vs Gemma 4 31B

Across our 12-test suite, Gemma 4 31B wins more individual tests (4 wins) while Gemini 2.5 Pro wins 2 tests; 6 tests are ties. Detailed walk-through:

• Strategic analysis: Gemma 4 31B scores 5 (tied for 1st with 25 others out of 54 tested) vs Gemini 2.5 Pro's 4 (rank 27 of 54). For tasks requiring nuanced tradeoff reasoning with numbers, Gemma 4 31B is stronger in our testing.
• Constrained rewriting: Gemma 4 31B scores 4 (rank 6 of 53) vs Gemini 2.5 Pro's 3 (rank 31 of 53). If you must compress text within hard character limits, Gemma 4 31B produced better results in our tests.
• Safety calibration: Gemma 4 31B scores 2 (rank 12 of 55) vs Gemini 2.5 Pro's 1 (rank 32 of 55). Gemma 4 31B better balances refusals vs allowances on risky prompts in our testing.
• Agentic planning: Gemma 4 31B scores 5 (tied for 1st) vs Gemini 2.5 Pro's 4 (rank 16). For goal decomposition and failure recovery, Gemma 4 31B leads.
• Creative problem solving: Gemini 2.5 Pro scores 5 (tied for 1st) vs Gemma 4 31B's 4 (rank 9). For non-obvious, feasible ideation, Gemini 2.5 Pro is stronger in our testing.
• Long context: Gemini 2.5 Pro scores 5 (tied for 1st with 36 others out of 55) vs Gemma 4 31B's 4 (rank 38 of 55). Gemini 2.5 Pro's 1,048,576-token context window and top rank mean it performs far better on retrieval/summary tasks across 30K+ tokens in our benchmarks.
• Ties (structured_output, tool_calling, faithfulness, classification, persona_consistency, multilingual): both models scored identically on these tests in our suite (e.g., structured_output 5/tied for 1st; tool_calling 5/tied for 1st; faithfulness 5/tied for 1st). In practice this means both models are similar for JSON schema adherence, function selection, sticking to sources, routing/classification, persona maintenance, and multilingual outputs. Supplementary external results: in our payload Gemini 2.5 Pro also reports external scores — 57.6% on SWE-bench Verified and 84.2% on AIME 2025 (Epoch AI) — which support its strengths on some coding and math tasks. Gemma 4 31B has no external sweep scores in the payload. Overall, Gemma 4 31B leads on planning, constrained rewriting, and safety; Gemini 2.5 Pro leads on long-context retrieval and creative problem solving; many practical dimensions are tied.

Per the payload, Gemini 2.5 Pro charges $1.25 per mTok for input and $10 per mTok for output; Gemma 4 31B charges $0.13 input and $0.38 output. Per-million-token math (1 mTok = 1,000 tokens):

• Gemini 2.5 Pro: $1,250 input / $10,000 output per 1M tokens. If your usage is 50% input / 50% output, 1M total tokens ≈ $5,625. Ten million tokens ≈ $56,250; 100M ≈ $562,500.
• Gemma 4 31B: $130 input / $380 output per 1M tokens. At 50/50 split, 1M total ≈ $255. Ten million ≈ $2,550; 100M ≈ $25,500. At scale the gap is enormous: for a 50/50 1M token workload, Gemini 2.5 Pro costs ~$5,625 vs Gemma 4 31B ~$255 (≈$5,370 difference). The payload's priceRatio is ~26.32×. High-volume consumer apps, chatbots, and companies with large inference budgets should prefer Gemma 4 31B for cost efficiency; teams needing the best long-context and creative outputs and who can absorb very high per-token spend may justify Gemini 2.5 Pro.

Choose Gemma 4 31B if: you need a cost‑efficient production model for agentic workflows, strategic analysis, constrained rewriting, or better safety calibration — it wins 4 tests in our suite, scores 5 on strategic_analysis, agentic_planning, and tied-1st on faithfulness and tool_calling, and costs $0.38 per mTok output. Choose Gemini 2.5 Pro if: your priority is extreme long-context work (1,048,576 token window), top creative problem solving, and highest-ranked long-context retrieval — it scores 5 on long_context and creative_problem_solving — and you can accept ~26× higher output spend ($10 per mTok). If cost is a major constraint, prefer Gemma 4 31B; if performance on very large contexts or elite ideation matters more than cost, pick Gemini 2.5 Pro.