Gemini 3.1 Pro Preview vs Mistral Medium 3.1

Across our 12-test internal suite, Gemini 3.1 Pro Preview wins 3 benchmarks outright, Mistral Medium 3.1 wins 2, and 7 are ties.

Where Gemini 3.1 Pro Preview wins:

• Structured output (5 vs 4): Gemini 3.1 Pro Preview achieves a perfect 5/5 on JSON schema compliance, tied for 1st with 24 other models out of 54 tested. Mistral Medium 3.1 scores 4/5, placing it rank 26 of 54. In practice, this matters for any system that parses model output programmatically — Gemini 3.1 Pro Preview is the more reliable choice for strict schema enforcement.
• Creative problem solving (5 vs 3): This is the sharpest gap in the comparison. Gemini 3.1 Pro Preview scores 5/5, tied for 1st with 7 others out of 54 tested. Mistral Medium 3.1 scores only 3/5, placing it rank 30 of 54 — below the field median of 4. For tasks requiring non-obvious, feasible ideas, Gemini 3.1 Pro Preview has a clear advantage.
• Faithfulness (5 vs 4): Gemini 3.1 Pro Preview scores 5/5, tied for 1st with 32 others out of 55 tested. Mistral Medium 3.1 scores 4/5, rank 34 of 55. For RAG applications and summarization where staying grounded in source material is critical, Gemini 3.1 Pro Preview carries less hallucination risk in our tests.

Where Mistral Medium 3.1 wins:

• Constrained rewriting (5 vs 4): Mistral Medium 3.1 scores 5/5, tied for 1st with 4 others out of 53 tested — a genuine top-tier result. Gemini 3.1 Pro Preview scores 4/5, rank 6 of 53. For tasks like compressing copy to hard character limits or reformatting content under strict constraints, Mistral Medium 3.1 is the stronger model.
• Classification (4 vs 2): The most dramatic win for Mistral Medium 3.1. It scores 4/5 on accurate categorization and routing, tied for 1st with 29 others out of 53 tested. Gemini 3.1 Pro Preview scores only 2/5, placing it rank 51 of 53 — near the bottom of the entire field. This is a significant weakness for routing, tagging, or intent-detection pipelines.

Ties (7 benchmarks): Both models score identically on strategic analysis (5 vs 5), tool calling (4 vs 4), long context (5 vs 5), safety calibration (2 vs 2), persona consistency (5 vs 5), agentic planning (5 vs 5), and multilingual (5 vs 5). The tie on agentic planning is noteworthy — both are tied for 1st among 54 models tested, suggesting comparable capability for multi-step agent workflows.

External benchmark — AIME 2025 (Epoch AI): Gemini 3.1 Pro Preview scores 95.6% on AIME 2025, ranking 2nd of 23 models tested. This places it above the dataset median of 83.9% and above the 75th percentile of 90%. Mistral Medium 3.1 has no AIME 2025 score in our data. While AIME 2025 tests math olympiad reasoning specifically, a score this high correlates with strong reasoning across complex multi-step problems.

The price gap here is stark: Gemini 3.1 Pro Preview charges $2.00/M input and $12.00/M output tokens, while Mistral Medium 3.1 runs $0.40/M input and $2.00/M output — a 5× gap on input and 6× on output. At 1M output tokens/month, that's $12 vs $2 — a $10 difference that's easy to ignore. At 10M tokens/month the gap is $100 vs $20, still manageable for most teams. But at 100M output tokens/month — a realistic scale for production APIs, chatbots, or content pipelines — you're looking at $1,200 vs $200 per month, a $1,000/month difference for the same volume. For applications where Gemini 3.1 Pro Preview's wins (creative problem solving, structured output, faithfulness) are core to the product, the premium is defensible. For commodity tasks like classification and text rewriting, Mistral Medium 3.1 matches or exceeds it at a fraction of the cost.

Choose Gemini 3.1 Pro Preview if: your application depends on creative ideation, strict JSON schema compliance, or RAG/summarization faithfulness — and you can justify $12/M output tokens. It's also the clear choice for math-heavy or reasoning-intensive workloads, given its 95.6% AIME 2025 score (rank 2 of 23, Epoch AI). Its 1M-token context window (vs 131K for Mistral Medium 3.1) makes it the only viable option for extremely long-document tasks. It also supports richer input modalities: text, image, file, audio, and video.

Choose Mistral Medium 3.1 if: you're running classification, intent routing, or text rewriting at scale. Its 4/5 classification score (tied for 1st among 53 models) versus Gemini 3.1 Pro Preview's 2/5 makes it the correct choice for any pipeline where accurate categorization is the primary task. At $2/M output tokens, it's also the right default for cost-sensitive production workloads where the benchmark gaps above don't apply to your use case.