Gemma 4 31B vs GPT-5 Mini

Across our 12-test suite, wins/ties break down as: Gemma 4 31B wins tool calling and agentic planning; GPT-5 Mini wins long context and safety calibration; eight tests tie (structured output, strategic analysis, constrained rewriting, creative problem solving, faithfulness, classification, persona consistency, multilingual). Detailed points: - Tool calling: Gemma scores 5 vs GPT-5 Mini 3; Gemma is tied for 1st in our rankings ("tied for 1st with 16 other models out of 54 tested"), while GPT-5 Mini ranks 47 of 54. This matters when selecting functions, arguments, and sequencing for API/tool integrations—Gemma will pick and populate calls more accurately in our tests. - Agentic planning: Gemma 5 vs GPT-5 Mini 4; Gemma is tied for 1st (with 14 others) — better at goal decomposition and failure recovery in our testing. - Long context: GPT-5 Mini scores 5 vs Gemma 4; GPT-5 Mini is tied for 1st on long context ("tied for 1st with 36 other models out of 55 tested"), and GPT-5 Mini also has a larger context_window (400,000 vs Gemma's 262,144), which aligns with stronger retrieval over 30k+ tokens in our benchmark. - Safety calibration: GPT-5 Mini 3 vs Gemma 2; GPT-5 Mini ranks 10 of 55 (2 models share this score) vs Gemma's rank 12 of 55 (20 models share this score), so GPT-5 Mini refuses or permits appropriately more often in our tests. - Ties: both models score 5 on structured output, strategic analysis, faithfulness, persona consistency, classification, and multilingual, and 4 on constrained rewriting and creative problem solving; these ties indicate comparable performance on JSON/schema adherence, nuanced tradeoffs, sticking to source material, consistent personas, routing/classification, and multilingual outputs. - External math/coding signals: GPT-5 Mini has external results on third-party benchmarks: 64.7% on SWE-bench Verified, 97.8% on MATH Level 5, and 86.7% on AIME 2025 (these external scores are from Epoch AI). Gemma has no external scores in the payload to compare. In sum, Gemma is the stronger pick where tool selection and agentic workflows are primary; GPT-5 Mini is stronger for very long context tasks and has demonstrated high math scores on Epoch AI benchmarks.

Costs shown are per mTok (1,000 tokens). We assume a 50/50 split of input vs output tokens to model total billable traffic. At 1M tokens/month (1,000 mTok): Gemma 4 31B costs ~$255 (500 mTok input × $0.13 = $65; 500 mTok output × $0.38 = $190). GPT-5 Mini costs ~$1,125 (500 × $0.25 = $125; 500 × $2.00 = $1,000). At 10M tokens/month: Gemma ≈ $2,550; GPT-5 Mini ≈ $11,250. At 100M tokens/month: Gemma ≈ $25,500; GPT-5 Mini ≈ $112,500. The cost gap grows linearly with volume: you’ll pay ~4.4× more for GPT-5 Mini in this 50/50 scenario. High-volume deployments (SaaS, indexing, heavy chat traffic) should care: Gemma materially reduces recurring spend, while GPT-5 Mini demands a much larger budget for similar general-quality capabilities.

Choose Gemma 4 31B if you need: - Reliable tool calling and function selection (Gemma 5 vs GPT-5 Mini 3 on tool calling; Gemma tied for 1st among tested models). - Strong agentic planning (Gemma 5 vs GPT-5 Mini 4). - A much lower recurring bill (output $0.38 vs $2.00). Ideal for high-volume API integrations, multi-step agent workflows, and multimodal input where cost matters. Choose GPT-5 Mini if you need: - Best-in-class long-context handling (GPT-5 Mini scores 5 and ties for 1st on long context; 400,000 token window). - Better safety calibration in our tests (GPT-5 Mini 3 vs Gemma 2). - Superior external math performance (MATH Level 5 97.8%, AIME 2025 86.7%, SWE-bench Verified 64.7% per Epoch AI). Good for applications where preserving very long context or math problem solving outweighs the much higher per-token cost.