Gemini 3 Flash Preview vs Gemma 4 31B

Test-by-test comparison (our 12-test suite):

• structured_output: tie (both 5). Both models are top-ranked here (tied for 1st of 54). Expect reliable JSON/schema compliance on either model.
• strategic_analysis: tie (both 5). Both rank tied for 1st of 54 — good for numeric tradeoff reasoning either way.
• constrained_rewriting: tie (both 4). Both are ranked 6 of 53 for compression within hard limits — acceptable for fixed-length rewrites.
• creative_problem_solving: Gemini 3 Flash Preview wins (5 vs 4). Gemini ranks tied for 1st (display: tied for 1st with 7 others) while Gemma ranks 9 of 54; this matters for non-obvious, specific idea generation where Gemini produced more feasible/novel proposals in our testing.
• tool_calling: tie (both 5). Both tied for 1st of 54 (tied with 16 others) — function selection and sequencing should be comparable.
• faithfulness: tie (both 5). Both models tied for 1st of 55 — low hallucination tendency on source-limited tasks in our tests.
• classification: tie (both 4). Both tied for 1st of 53 — routing and labeling quality comparable.
• long_context: Gemini 3 Flash Preview wins (5 vs 4). Gemini is tied for 1st of 55 (tied with 36 others); Gemma is ranked 38 of 55. In practice this means Gemini handled retrieval/QA over 30K+ token contexts more accurately in our benchmarks.
• safety_calibration: Gemma 4 31B wins (2 vs 1). Gemma ranks 12 of 55 vs Gemini at 32 of 55: Gemma is meaningfully better at refusing harmful prompts while allowing legitimate ones in our tests.
• persona_consistency: tie (both 5). Both tied for 1st of 53 — both maintain character and resist injection similarly.
• agentic_planning: tie (both 5). Both tied for 1st of 54 — goal decomposition and recovery comparable.
• multilingual: tie (both 5). Both tied for 1st of 55 — equivalent quality in non-English output in our tests. External benchmarks (supplementary): Gemini 3 Flash Preview scores 75.4% on SWE-bench Verified and 92.8% on AIME 2025 (Epoch AI), ranking respectively 3 of 12 and 5 of 23 on those third-party tests. Gemma 4 31B has no external SWE/MATH scores in the payload. Overall, Gemini wins the plurality of internal tests that matter for large-context reasoning and creative tasks; Gemma wins the safety calibration test and matches Gemini across many core capabilities.

Combined input+output cost per million tokens: Gemini 3 Flash Preview = $0.50 + $3.00 = $3.50/M; Gemma 4 31B = $0.13 + $0.38 = $0.51/M (price ratio ≈ 7.89). At 1M tokens/month: $3.50 vs $0.51. At 10M: $35.00 vs $5.10. At 100M: $350.00 vs $51.00. The gap matters for high-volume applications (autocomplete, high-traffic chat, large-scale inference)—teams at 10M+ tokens/month will see tens to hundreds of dollars difference monthly; at 100M it becomes a significant operational cost. Choose Gemini when its superior long-context and creative/problem-solving capabilities justify paying ~7.9x; choose Gemma 4 31B when cost per token is the overriding constraint.

Choose Gemini 3 Flash Preview if you need the best long-context handling and higher creative/problem-solving capability for multi-turn agentic workflows, coding assistance, or research over very large documents — you pay roughly $3.50 per million tokens for that edge. Choose Gemma 4 31B if monthly token costs, safer refusal behavior, and the 256K context window are higher priorities — it costs about $0.51 per million tokens and scored better on safety_calibration in our tests.