Grok 3 vs Mistral Medium 3.1

Across our 12-test suite, Grok 3 wins 2 benchmarks, Mistral Medium 3.1 wins 1, and they tie on 9. Neither model has a wide-margin, across-the-board lead.

Where Grok 3 leads:

• Structured output (5 vs 4): Grok 3 scores 5/5, tied for 1st with 24 other models out of 54 tested. Mistral Medium 3.1 scores 4/5, rank 26 of 54. For JSON schema compliance and format adherence at scale, Grok 3 is the more reliable option.
• Faithfulness (5 vs 4): Grok 3 scores 5/5, tied for 1st with 32 others out of 55 tested. Mistral Medium 3.1 scores 4/5, rank 34 of 55. This measures how well a model sticks to source material without hallucinating — critical for RAG pipelines and document summarization.

Where Mistral Medium 3.1 leads:

• Constrained rewriting (5 vs 3): This is the sharpest gap in the comparison. Mistral Medium 3.1 scores 5/5, tied for 1st with just 4 other models out of 53 tested — a genuinely elite result. Grok 3 scores 3/5, rank 31 of 53. For tasks requiring compression within hard character limits (ad copy, social content, SMS), Mistral Medium 3.1 is clearly better.

Where they tie across 9 tests:

• Strategic analysis (5/5 each): Both tied for 1st with 25 others out of 54 — top-tier nuanced reasoning.
• Agentic planning (5/5 each): Both tied for 1st with 14 others out of 54 — strong for goal decomposition and multi-step agent workflows.
• Long context (5/5 each): Both tied for 1st with 36 others out of 55 — equivalent retrieval accuracy at 30K+ tokens.
• Multilingual (5/5 each): Both tied for 1st with 34 others out of 55 — top-tier non-English quality.
• Persona consistency (5/5 each): Both tied for 1st with 36 others out of 53.
• Tool calling (4/4 each): Both rank 18 of 54, tied with 28 other models — solid but not differentiated.
• Classification (4/4 each): Both tied for 1st with 29 others out of 53.
• Safety calibration (2/2 each): Both rank 12 of 55 — identical and below the field median of 2, though 20 models share this score.
• Creative problem solving (3/3 each): Both rank 30 of 54 — below the field median of 4. Neither excels at generating non-obvious, feasible ideas.

Note: the data payload does not include external benchmark scores (SWE-bench, AIME 2025, MATH Level 5) for either model, so no third-party comparisons are available here.

Grok 3 is priced at $3.00/M input and $15.00/M output tokens. Mistral Medium 3.1 runs $0.40/M input and $2.00/M output — a 7.5x gap on the output side, which is where most costs accumulate. At 1M output tokens/month, that's $15 vs $2 — a $13 difference that barely registers. At 10M tokens/month, it's $150 vs $20, a $130 gap that starts to matter for lean teams. At 100M tokens/month, you're looking at $1,500 vs $200 — a $1,300 monthly difference that demands justification. Given that the two models tie on 9 of 12 benchmarks in our testing, the cost case for Grok 3 has to rest entirely on its leads in structured output and faithfulness. If those specific capabilities are core to your workload, the premium may be worth it. If your pipeline is more generalist — planning, classification, multilingual, tool calling — Mistral Medium 3.1 delivers equivalent scores at a fraction of the cost.

Choose Grok 3 if:

• Your pipeline depends on strict JSON schema compliance and structured output reliability (scored 5/5 vs 4/5 in our tests).
• You're building RAG or document-grounded applications where hallucination risk matters most — Grok 3 scores 5/5 on faithfulness vs Mistral Medium 3.1's 4/5.
• Your output volume is modest enough that the 7.5x price premium ($15 vs $2 per million output tokens) doesn't materially affect your budget.

Choose Mistral Medium 3.1 if:

• Constrained rewriting is a core task — Mistral Medium 3.1 scores 5/5 and ranks in the top 5 of 53 models; Grok 3 scores 3/5.
• You need image input support — Mistral Medium 3.1's text+image->text modality is not available on Grok 3 per the payload.
• You're running at scale (10M+ output tokens/month) where the $13/M output token savings compounds significantly.
• Your use case maps to any of the 9 tied benchmarks — you get equivalent performance for 87% less on output costs.