Gemma 4 31B vs Ministral 3 14B 2512

Summary: Across our 12-test suite Gemma 4 31B wins 7 categories, ties 5, and Ministral 3 14B 2512 wins none. Detailed walk-through:

• Structured output: Gemma 5 vs Ministral 4. Gemma is tied for 1st of 54 (tied with 24 others); Ministral ranks 26 of 54. Practically, Gemma is the safer choice when strict JSON/schema adherence and machine-parseable outputs matter.

• Strategic analysis: Gemma 5 vs Ministral 4. Gemma ties for 1st of 54; Ministral ranks 27 of 54. In tasks requiring nuanced tradeoffs and numeric reasoning, Gemma produces more reliable stepwise reasoning in our tests.

• Tool calling: Gemma 5 vs Ministral 4. Gemma tied for 1st of 54; Ministral rank 18 of 54. This indicates Gemma better selects functions, orders calls, and populates arguments in our function-invocation scenarios.

• Faithfulness: Gemma 5 vs Ministral 4. Gemma tied for 1st of 55; Ministral rank 34 of 55. For tasks where sticking to source material (avoiding hallucination) is critical, Gemma scored higher in our runs.

• Agentic planning: Gemma 5 vs Ministral 3. Gemma tied for 1st of 54; Ministral ranks 42 of 54. This is one of the largest gaps — Gemma outperforms on goal decomposition and recovery strategies in our benchmarks.

• Multilingual: Gemma 5 vs Ministral 4. Gemma tied for 1st of 55; Ministral ranks 36 of 55. Non-English parity favors Gemma in our tests.

• Safety calibration: Gemma 2 vs Ministral 1. Gemma ranks 12 of 55 (tied with 19 others); Ministral ranks 32 of 55. Both scores are low in absolute terms (safety calibration is a hard area across models), but Gemma refused or permitted appropriately slightly more often in our safety prompts.

Ties (no clear winner in our testing): constrained rewriting (4/4; both rank 6 of 53), creative problem solving (4/4; both rank 9 of 54), classification (4/4; both tied for 1st among 53), long context (4/4; both rank 38 of 55), and persona consistency (5/5; both tied for 1st). These ties mean either model can be viable for those tasks; inspect other differentiators (cost, supported parameters, modality) when choosing.

Modality and capabilities: Gemma 4 31B lists modality 'text+image+video->text' and supports parameters like include_reasoning/reasoning and structured outputs; Ministral 3 14B 2512 lists 'text+image->text' and supports logprobs/top_logprobs. Those differences explain some practical tradeoffs: Gemma is tuned for richer multimodal and reasoning workflows in our tests; Ministral exposes logprobs which can help debugging or selective sampling.

Costs are quoted per mTok (1 mTok = 1,000 tokens). Gemma 4 31B: input $0.13/mTok, output $0.38/mTok. Ministral 3 14B 2512: input $0.20/mTok, output $0.20/mTok. Example (50/50 input/output split):

• 1M tokens (500 mTok input + 500 mTok output): Gemma = $65 + $190 = $255; Ministral = $100 + $100 = $200.
• 10M tokens (5,000 mTok each): Gemma = $650 + $1,900 = $2,550; Ministral = $2,000.
• 100M tokens (50,000 mTok each): Gemma = $6,500 + $19,000 = $25,500; Ministral = $20,000. If your workload is output-heavy (most production generation), Ministral saves $0.18/mTok on output and will be substantially cheaper (e.g., single 1M output tokens: Gemma $380 vs Ministral $200). If you have input-heavy pipelines (large contexts uploaded as input), Gemma is cheaper on input ($0.13 vs $0.20). Teams with millions of output tokens per month (chat, content generation) should care about the $0.18/mTok output gap; research or retrieval-heavy workflows should factor in Gemma's lower input cost.

Choose Gemma 4 31B if you need best-in-suite structured outputs, tool calling, agentic planning, higher faithfulness and stronger multilingual behavior in our tests — ideal for production agents, strict API outputs, and multilingual assistants. Choose Ministral 3 14B 2512 if raw per-token output cost is the primary constraint (output $0.20/mTok vs Gemma $0.38/mTok) and you can accept slightly lower performance on tool calling, planning, and faithfulness. If your workload is input-heavy (large contexts), Gemma's lower input price ($0.13 vs $0.20) narrows the cost gap.