Gemma 4 26B A4B vs Mistral Small 3.1 24B

Across our 12-test suite Gemma 4 26B A4B wins 9 categories, Mistral Small 3.1 24B wins 0, and 3 tests tie. Detailed walk-through (scores are our 1–5 internal ratings):

• Structured output: Gemma 5 vs Mistral 4. Gemma is tied for 1st in our rankings ("tied for 1st with 24 other models out of 54 tested"), meaning Gemma is a safer pick when you need strict JSON/schema compliance and format adherence.
• Tool calling: Gemma 5 vs Mistral 1. Gemma ranks "tied for 1st with 16 other models"; Mistral ranks 53 of 54 and has a quirk flagged "no_tool calling". For function selection, argument accuracy, or multi-step tool sequencing Gemma is decisively better in our testing.
• Faithfulness: Gemma 5 vs Mistral 4. Gemma is "tied for 1st with 32 other models out of 55 tested," so it more reliably sticks to source material and avoids hallucination in our tests.
• Classification: Gemma 4 vs Mistral 3. Gemma is "tied for 1st with 29 other models out of 53," which matters for routing and content tagging accuracy.
• Persona consistency: Gemma 5 vs Mistral 2. Gemma is "tied for 1st with 36 other models" — important when maintaining character or avoiding prompt-injection drift.
• Agentic planning: Gemma 4 vs Mistral 3. Gemma ranks 16 of 54 (26 models share this), so it better decomposes goals and recovers from failures.
• Creative problem solving: Gemma 4 vs Mistral 2. Gemma ranks 9 of 54 (21 models share this), so it produces more non-obvious, feasible ideas in our tests.
• Strategic analysis: Gemma 5 vs Mistral 3. Gemma is "tied for 1st with 25 other models," showing stronger nuanced tradeoff reasoning with numbers.
• Multilingual: Gemma 5 vs Mistral 4. Gemma is "tied for 1st with 34 other models," so non-English parity favors Gemma in our testing.
• Long context: tie 5 vs 5. Both models are "tied for 1st with 36 other models out of 55 tested" — both handle retrieval at 30K+ tokens similarly for the tested scenarios.
• Constrained rewriting: tie 3 vs 3. Both scored equally on compression within hard limits in our tests.
• Safety calibration: tie 1 vs 1. Both models scored low on safety calibration in our testing (rank 32 of 55 for each), indicating similar refusal/allow behaviors in these probes. In short: Gemma leads on structured outputs, tool chaining, faithfulness, multilingual, creative and strategic tasks. Mistral’s only parity is long-context retrieval; otherwise it trails and is more expensive per the payload.

The payload lists Gemma 4 26B A4B at $0.08 input / $0.35 output per mTok and Mistral Small 3.1 24B at $0.35 input / $0.56 output per mTok. Using the per-mTok prices to scale, per 1M tokens: Gemma input ≈ $80, output ≈ $350; Mistral input ≈ $350, output ≈ $560. For a 50/50 input/output mix per month: Gemma ≈ $215 per 1M tokens ($2,150 per 10M, $21,500 per 100M); Mistral ≈ $455 per 1M tokens ($4,550 per 10M, $45,500 per 100M). The payload also gives a priceRatio of 0.625 (Gemma costs ~62.5% of Mistral). Teams with heavy token volumes (10M–100M+/month) should care most: choosing Gemma can save roughly $240 per 1M tokens on a 50/50 I/O mix, compounding to large savings at scale.

Choose Gemma 4 26B A4B if you need reliable JSON/schema adherence, tool calling (function selection/argument accuracy), stronger faithfulness, multilingual parity, or agentic/strategic reasoning — it won 9 of 12 benchmarks in our testing and costs ~62.5% of Mistral. Choose Mistral Small 3.1 24B only if you specifically require its particular API or ecosystem and can accept weaker tool calling, lower scores across creative/strategic tasks, and the higher token costs (Mistral is ~1.6x–2.1x more expensive per I/O token in the payload).