Grok 4.1 Fast vs Mistral Medium 3.1

Across our 12-test suite, Grok 4.1 Fast wins 3 benchmarks, Mistral Medium 3.1 wins 3, and 6 are tied.

Grok 4.1 Fast wins:

• Structured output: 5 vs 4. Grok 4.1 Fast ties for 1st among 54 models; Mistral Medium 3.1 ranks 26th. This matters for any pipeline that depends on reliable JSON schema compliance — Grok 4.1 Fast is more dependable here.
• Faithfulness: 5 vs 4. Grok 4.1 Fast ties for 1st among 55 models; Mistral Medium 3.1 ranks 34th. Faithfulness measures how well a model sticks to source material without hallucinating — critical for RAG applications, summarization, and document Q&A.
• Creative problem solving: 4 vs 3. Grok 4.1 Fast ranks 9th of 54; Mistral Medium 3.1 ranks 30th. The gap is meaningful — Grok 4.1 Fast produces more non-obvious, feasible ideas in our testing.

Mistral Medium 3.1 wins:

• Agentic planning: 5 vs 4. Mistral Medium 3.1 ties for 1st among 54 models (15 models share this score); Grok 4.1 Fast ranks 16th. Agentic planning covers goal decomposition and failure recovery — the backbone of multi-step AI agents.
• Constrained rewriting: 5 vs 4. Mistral Medium 3.1 ties for 1st among 53 models (only 5 models share this score, making it a genuine differentiator); Grok 4.1 Fast ranks 6th. This covers compression within hard character limits — useful for copywriting, ad generation, and SEO tasks.
• Safety calibration: 2 vs 1. Mistral Medium 3.1 ranks 12th of 55; Grok 4.1 Fast ranks 32nd. Both are below the field median (p50 = 2), but Mistral Medium 3.1 is meaningfully better at refusing harmful requests while permitting legitimate ones.

Tied tests (6): Strategic analysis (both 5/5), tool calling (both 4/5), classification (both 4/5), long context (both 5/5), persona consistency (both 5/5), and multilingual (both 5/5). On long context, both models tie for 1st among 55 tested — though Grok 4.1 Fast's 2M context window dwarfs Mistral Medium 3.1's 131K, a structural difference that doesn't show up in the score but matters for very long documents.

Overall, Grok 4.1 Fast's wins cluster around output quality and reliability (structured output, faithfulness), while Mistral Medium 3.1's wins cluster around workflow orchestration and content control.

Grok 4.1 Fast costs $0.20/MTok input and $0.50/MTok output. Mistral Medium 3.1 costs $0.40/MTok input and $2.00/MTok output — twice the input price and four times the output price. In practice, output costs dominate most workloads. At 1M output tokens/month, you pay $0.50 with Grok 4.1 Fast vs $2.00 with Mistral Medium 3.1 — a $1.50 difference. Scale to 10M output tokens and that gap becomes $15,000/month; at 100M tokens it's $150,000/month. For developers running high-volume pipelines — customer support bots, document processing, content generation — Grok 4.1 Fast's cost structure is a major operational advantage. Mistral Medium 3.1's pricing is harder to justify unless the specific benchmark wins (agentic planning, constrained rewriting, safety calibration) are business-critical for your use case.

Choose Grok 4.1 Fast if: you need reliable structured output (JSON/schema-heavy pipelines), RAG or summarization workflows where faithfulness to source material is critical, high-volume production deployments where the $1.50 output cost difference per million tokens adds up fast, tasks requiring long context beyond 131K tokens (up to 2M), or any use case where creative problem solving quality matters. Also choose Grok 4.1 Fast if budget is a constraint — it delivers equal or better scores on 9 of 12 tests for a fraction of the output cost.

Choose Mistral Medium 3.1 if: you are building multi-step agentic systems where planning and failure recovery (agentic planning score: 5/5, tied for 1st) are the primary bottleneck; your workflow depends on tight constrained rewriting (ad copy, character-limited content) where Mistral Medium 3.1 is among only 5 models to hit the top score; or your deployment context requires stronger safety calibration and content filtering. The higher price is a real cost at scale, so Mistral Medium 3.1 makes most sense in lower-volume, agentic-heavy use cases where its specific benchmark wins justify the premium.