Gemma 4 31B vs Mistral Small 3.2 24B

Across our 12-test suite Gemma 4 31B wins 10 tasks, Mistral Small 3.2 24B wins 0, and they tie on 2. Detailed comparisons (our scores):

• Tool calling: Gemma 5 vs Mistral 4. Gemma is tied for 1st on tool calling ("tied for 1st with 16 other models out of 54 tested"), which matters for accurate function selection, argument formatting, and sequencing in integrations.
• Structured output: Gemma 5 vs Mistral 4. Gemma is tied for 1st on structured output ("tied for 1st with 24 other models out of 54 tested"), meaning better JSON/schema adherence for programmatic consumers.
• Strategic analysis: Gemma 5 vs Mistral 2. Gemma is tied for 1st on strategic analysis ("tied for 1st with 25 other models out of 54 tested"), reflecting notably stronger nuanced tradeoff reasoning and numeric handling.
• Creative problem solving: Gemma 4 vs Mistral 2 (Gemma ranks 9 of 54, Mistral ranks 47 of 54), so Gemma produces more specific, feasible ideas in our tests.
• Faithfulness: Gemma 5 vs Mistral 4 (Gemma tied for 1st), so Gemma better sticks to source material without hallucination in our runs.
• Classification: Gemma 4 vs Mistral 3 (Gemma tied for 1st), indicating more accurate routing/categorization in our tests.
• Persona consistency: Gemma 5 vs Mistral 3 (Gemma tied for 1st), so prompts that require strict voice or role adherence stayed more consistent with Gemma.
• Agentic planning: Gemma 5 vs Mistral 4 (Gemma tied for 1st), meaning Gemma decomposes goals and recovery steps more robustly in our scenarios.
• Multilingual: Gemma 5 vs Mistral 4 (Gemma tied for 1st), so non-English parity favored Gemma in our evaluation.
• Safety calibration: Gemma 2 vs Mistral 1 — Gemma refused or calibrated risky prompts more appropriately in our tests, though both are middling by our shared distribution.
• Ties: constrained rewriting 4/4 (both rank 6 of 53) and long context 4/4 (both rank 38 of 55). Note context window and modality differences from the payload: Gemma has a 262,144-token window and supports text+image+video→text; Mistral has a 128,000-token window and supports text+image→text. Those platform features explain why Gemma leads on multimodal, tool-calling, and agentic planning in our benchmarks.

Costs in the payload are per mTok (per 1,000 tokens). Gemma 4 31B: input $0.13, output $0.38 per mTok. Mistral Small 3.2 24B: input $0.075, output $0.20 per mTok. Using a simple 50% input / 50% output example: per 1M tokens (1,000 mTok total) Gemma costs $255 (0.13×500 + 0.38×500) and Mistral costs $137.50 (0.075×500 + 0.20×500) — a $117.50 difference per 1M. At 10M tokens that gap is $1,175; at 100M it's $11,750. If your workload is output-heavy (90% output), Gemma costs $355 per 1M vs Mistral $187.50 per 1M (a $167.50 gap). Who should care: teams running high-volume production APIs, batch generation, or large-scale multimodal pipelines will feel the difference; small projects or experimentation workloads will favor Mistral for cost savings.

Choose Gemma 4 31B if you need best-in-class tool-calling, structured-output compliance, strategic analysis, stronger multilingual and persona consistency, large context windows (262K), or multimodal (video) inputs — and you accept ~1.9× higher per-token costs. Choose Mistral Small 3.2 24B if you prioritize lower per-token cost (input $0.075 / output $0.20 per mTok), want a capable instruction-following model for less expensive deployments, or are running high-volume, cost-sensitive workloads where Gemma's quality gains don't justify the price gap.