Gemma 4 26B A4B vs Ministral 3 14B 2512

We ran the two models across our 12‑test suite and Gemma 4 26B A4B wins 7 tests, Ministral 3 14B 2512 wins 1, and 4 tests tie. Detailed walk‑through: 1) structured output: Gemma 5 vs Ministral 4 — Gemma is tied for 1st (tied with 24 others of 54) so it’s the safer choice when you need strict JSON/schema compliance; Ministral ranks 26 of 54. 2) strategic analysis: Gemma 5 vs Ministral 4 — Gemma ties for 1st (display tied with 25 others), useful for nuanced tradeoff reasoning. 3) tool calling: Gemma 5 vs Ministral 4 — Gemma ties for 1st (tied with 16 others), meaning better function selection/argument accuracy for agentic flows; Ministral is rank 18. 4) faithfulness: Gemma 5 vs Ministral 4 — Gemma ties for 1st (tied with 32 others of 55), so it better sticks to source material in our tests; Ministral is rank 34. 5) long context: Gemma 5 vs Ministral 4 — Gemma ties for 1st (tied with 36 others of 55), indicating stronger retrieval at 30K+ token contexts; Ministral ranks 38. 6) agentic planning: Gemma 4 vs Ministral 3 — Gemma ranks 16 of 54 (26 models share that score) versus Ministral at rank 42, so Gemma decomposes goals and recovers from failures better in our tasks. 7) multilingual: Gemma 5 vs Ministral 4 — Gemma tied for 1st (tied with 34 others), giving it an edge on non‑English parity. 8) constrained rewriting: Gemma 3 vs Ministral 4 — Ministral wins and ranks 6 of 53, so it handles hard character/space compression better. 9) creative problem solving: tie 4/4 — both models rank similarly (each rank 9 of 54 tied with many models), so expect comparable idea generation. 10) classification: tie 4/4 — both are tied for 1st (tied with 29 others), so routing and categorization perform similarly. 11) persona consistency: tie 5/5 — both tie for 1st (tied with 36 others), so both maintain character well. 12) safety calibration: tie 1/1 — both score poorly here in our tests (rank 32 of 55), so neither is reliable at refusing harmful requests. In short: Gemma leads on structured outputs, long context, tool calling, faithfulness and overall strategic/agentic tasks; Ministral’s clear advantage is constrained rewriting and lower output pricing.

Costs in the payload are per mTok (per 1k tokens). Gemma 4 26B A4B: input $0.08/mTok, output $0.35/mTok. Ministral 3 14B 2512: input $0.20/mTok, output $0.20/mTok. Per 1M tokens (1,000 mTok): Gemma input = $80, output = $350; Ministral input = $200, output = $200. Example totals assuming a 50/50 input/output split per 1M tokens: Gemma ≈ $215, Ministral ≈ $200. For an output‑heavy 80% output / 20% input 1M tokens: Gemma ≈ $296, Ministral = $200 — a $96 gap per 1M. At 10M tokens multiply these totals by 10 (e.g., output‑heavy: Gemma ≈ $2,960 vs Ministral $2,000); at 100M multiply by 100. Who should care: high‑volume, output‑heavy apps (chat, large document generation, streaming) will see the largest absolute dollar difference; teams prioritizing structured outputs, long context, or tool integrations should weigh Gemma’s higher cost against its benchmark wins.

Choose Gemma 4 26B A4B if you need reliable JSON/schema outputs, long‑context retrieval (30K+), stronger tool calling and faithfulness — e.g., production agent integrations, document understanding at scale, or multilingual apps where correctness matters. Choose Ministral 3 14B 2512 if you need a lower per‑output token bill and better compressed/character‑limited rewriting — e.g., cost‑sensitive content generation, tight SMS/summary pipelines, or when constrained rewriting is critical.