Grok 4.20 vs Mistral Medium 3.1

Across our 12-test suite (scored 1–5), Grok 4.20 wins 4 benchmarks, Mistral Medium 3.1 wins 3, and they tie on 5. Note that many top scores are shared across many models — a 5/5 does not always mean uniquely best.

Where Grok 4.20 wins:

• Tool calling: 5 vs 4. Grok 4.20 ties for 1st among 54 models (with 16 others); Mistral Medium 3.1 ranks 18th (tied with 28 others). For function selection, argument accuracy, and call sequencing — the mechanics of agentic pipelines — Grok 4.20 is meaningfully more reliable in our tests.
• Faithfulness: 5 vs 4. Grok 4.20 ties for 1st among 55 models (32 others); Mistral Medium 3.1 ranks 34th. This gap matters for RAG and summarization tasks where sticking to source material is non-negotiable.
• Structured output: 5 vs 4. Grok 4.20 ties for 1st among 54 models (24 others); Mistral Medium 3.1 ranks 26th. JSON schema compliance and format adherence are stronger with Grok 4.20, which matters for any system parsing model output programmatically.
• Creative problem solving: 4 vs 3. Grok 4.20 ranks 9th of 54 (21 models share this score); Mistral Medium 3.1 ranks 30th of 54. The gap here represents a real difference in generating non-obvious, feasible ideas.

Where Mistral Medium 3.1 wins:

• Agentic planning: 5 vs 4. Mistral Medium 3.1 ties for 1st among 54 models (14 others); Grok 4.20 ranks 16th (tied with 25 others). Goal decomposition and failure recovery favor Mistral Medium 3.1 — an interesting split given Grok 4.20 leads on tool calling. Teams building multi-step agents should weigh both dimensions.
• Constrained rewriting: 5 vs 4. Mistral Medium 3.1 ties for 1st among 53 models (only 4 others share this top score, making it a stronger differentiator); Grok 4.20 ranks 6th (tied with 24 others). Compression within hard character limits — copywriting, summaries with strict length constraints — is Mistral Medium 3.1's standout strength.
• Safety calibration: 2 vs 1. Mistral Medium 3.1 ranks 12th of 55; Grok 4.20 ranks 32nd of 55. Both scores are below the median (p50 = 2 across all models), but Grok 4.20's score of 1 places it in the bottom quartile. This reflects a tendency to either over-refuse or under-refuse in our testing. For consumer-facing applications, this is a meaningful concern.

Ties (5 tests): Both models score identically on multilingual (both 5, tied for 1st among 55), strategic analysis (both 5, tied for 1st among 54), long context (both 5, tied for 1st among 55), persona consistency (both 5, tied for 1st among 53), and classification (both 4, tied for 1st among 53). These categories do not differentiate the two models.

Grok 4.20 costs $2.00/MTok input and $6.00/MTok output. Mistral Medium 3.1 costs $0.40/MTok input and $2.00/MTok output — 5× cheaper on input and 3× cheaper on output.

At 1M output tokens/month: Grok 4.20 runs $6.00 vs Mistral Medium 3.1's $2.00 — a $4 difference that's negligible for most teams.

At 10M output tokens/month: $60 vs $20 — a $40 gap that starts to matter for bootstrapped products.

At 100M output tokens/month: $600 vs $200 — a $400/month difference that makes Mistral Medium 3.1 the obvious default unless Grok 4.20's benchmark advantages justify the premium.

The context window also differs significantly: Grok 4.20 supports up to 2,000,000 tokens vs Mistral Medium 3.1's 131,072. If your workload involves very long documents or large codebase ingestion, Grok 4.20's context advantage may justify the higher cost. For standard enterprise API usage with typical prompt lengths, Mistral Medium 3.1's 131K window is sufficient and the cost savings are material.

Choose Grok 4.20 if:

• Your application depends on reliable tool/function calling — it scores 5 vs 4 and ranks in the top tier of 54 models in our tests.
• You're building RAG pipelines or document Q&A where faithfulness to source material is critical (5 vs 4, ranks 1st-tier vs 34th).
• You need programmatic output parsing and JSON schema compliance (structured output: 5 vs 4).
• You require a 2M-token context window for large codebase or document ingestion — Mistral Medium 3.1 caps at 131K.
• Cost is secondary and quality on the above dimensions justifies $2.00/$6.00 per MTok.

Choose Mistral Medium 3.1 if:

• You're operating at high token volumes (10M+ output tokens/month) where the 3× output cost difference ($2.00 vs $6.00/MTok) has real budget impact.
• You need agentic planning — goal decomposition and failure recovery — where it scores 5 vs Grok 4.20's 4, and ranks in the top tier of 54 models.
• You're producing content with strict length constraints: constrained rewriting is Mistral Medium 3.1's clearest differentiator, tying for 1st with only 4 other models out of 53.
• Safety calibration matters for your use case — Mistral Medium 3.1's score of 2 (rank 12 of 55) significantly outperforms Grok 4.20's 1 (rank 32 of 55).
• You need frequency_penalty and presence_penalty parameter support, which Mistral Medium 3.1 exposes but Grok 4.20 does not.