Gemma 4 26B A4B vs Ministral 3 8B 2512

Across our 12-test suite Gemma 4 26B A4B wins 8 benchmarks, Ministral 3 8B 2512 wins 1, and they tie on 3. Detailed walk-through (scores shown are our 1–5 internal grades):

• structured output: Gemma 5 (tied for 1st with 24 others out of 54) vs Ministral 4 (rank 26 of 54). In practice Gemma is best-in-class for JSON/schema compliance and strict format adherence.
• strategic analysis: Gemma 5 (tied for 1st) vs Ministral 3 (rank 36). Gemma handles nuanced trade-offs and numeric reasoning better for decision-focused prompts.
• constrained rewriting: Gemma 3 (rank 31) vs Ministral 5 (tied for 1st with 4 others). Ministral is substantially stronger when you must compress or rephrase under tight character limits.
• creative problem solving: Gemma 4 (rank 9) vs Ministral 3 (rank 30). Gemma produces more non-obvious, feasible ideas in our tests.
• tool calling: Gemma 5 (tied for 1st) vs Ministral 4 (rank 18). Gemma is more accurate at selecting functions, sequencing calls and filling arguments — important for agentic workflows and tool integrations.
• faithfulness: Gemma 5 (tied for 1st) vs Ministral 4 (rank 34). Gemma better sticks to source material and avoids hallucination in our testing.
• long context: Gemma 5 (tied for 1st) vs Ministral 4 (rank 38). Gemma is superior for retrieval and accuracy across 30K+ token contexts.
• agentic planning: Gemma 4 (rank 16) vs Ministral 3 (rank 42). Gemma decomposes goals and plans recovery steps more reliably.
• multilingual: Gemma 5 (tied for 1st) vs Ministral 4 (rank 36). Gemma delivers stronger non-English parity in our tests.
• persona consistency: both score 5 and tie (tied for 1st), so both maintain character and resist injection similarly well.
• classification: both score 4 and tie (tied for 1st), so routing/categorization are equivalent in our suite.
• safety calibration: both score 1 and tie (rank 32 of 55) — neither model scored well on safety calibration in our tests and will need system-level guardrails.

Bottom line from these scores: Gemma demonstrably wins the developer-focused, tool-integrated, long-context and faithfulness categories; Ministral’s standout is constrained rewriting plus a lower per-token output cost profile.

Costs from the payload: Gemma input $0.08/mTok and output $0.35/mTok; Ministral input $0.15/mTok and output $0.15/mTok. Price ratio (Gemma vs Ministral) = 2.333. Example costs assuming a 50/50 input/output split: 1M tokens (1,000 mTok) => Gemma $215 (5000.08 + 5000.35) vs Ministral $150 (1000*0.15). 10M tokens => Gemma $2,150 vs Ministral $1,500. 100M tokens => Gemma $21,500 vs Ministral $15,000. If your workload is output-heavy (e.g., chatbots generating long replies), Gemma’s $0.35/mTok output price drives the gap; if you mostly send short prompts and receive short outputs, the difference narrows but still favors Ministral on cost. Teams running millions of tokens/month or building consumer-facing apps should care about the gap; small-scale prototypes may accept Gemma’s premium for better structured output and tool calling.

Choose Gemma 4 26B A4B if: you need best-in-class structured output (5/5, tied for 1st), reliable tool calling (5/5, tied for 1st), long-context retrieval (5/5), strong faithfulness (5/5), multilingual parity, and robust agentic planning — and you can absorb higher output costs. Choose Ministral 3 8B 2512 if: you must compress/rewrite within strict character limits (5/5, tied for 1st), you’re cost-sensitive at scale (lower combined price per mTok), or you want a balanced, efficient model for mixed vision+text tasks while minimizing monthly spend.