Llama 4 Maverick vs Mistral Medium 3.1

Across our 12-test suite, Mistral Medium 3.1 wins 7 benchmarks outright and ties on 5, while Llama 4 Maverick wins none.

Where Mistral Medium 3.1 wins clearly:

• Strategic analysis: Mistral scores 5/5 (tied for 1st among 54 models) vs Llama 4 Maverick's 2/5 (rank 44 of 54). This is the widest gap in the suite — a 3-point difference on nuanced tradeoff reasoning with real numbers. If your use case involves analytical reports or decision support, this matters.
• Constrained rewriting: Mistral scores 5/5 (tied for 1st with 4 others out of 53) vs Llama 4 Maverick's 3/5 (rank 31 of 53). Compressing text within hard character limits is a common editorial and product copy task — Mistral handles it significantly better in our testing.
• Agentic planning: Mistral scores 5/5 (tied for 1st among 54 models) vs Llama 4 Maverick's 3/5 (rank 42 of 54). For goal decomposition and failure recovery in multi-step workflows, Mistral is meaningfully stronger.
• Long context: Mistral scores 5/5 (tied for 1st among 55 models) vs Llama 4 Maverick's 4/5 (rank 38 of 55). Mistral also has a 131K context window; Llama 4 Maverick offers a much larger 1,048,576-token window, but Mistral's retrieval accuracy at 30K+ tokens scores higher in our tests.
• Classification: Mistral scores 4/5 (tied for 1st among 53 models) vs Llama 4 Maverick's 3/5 (rank 31 of 53). Routing and categorization tasks favor Mistral.
• Multilingual: Mistral scores 5/5 (tied for 1st among 55 models) vs Llama 4 Maverick's 4/5 (rank 36 of 55). Both handle non-English well, but Mistral scores at the ceiling.
• Tool calling: Mistral scores 4/5 (rank 18 of 54). Llama 4 Maverick has no tool calling score in our data — a rate limit hit during testing on 2026-04-13 means results weren't recorded. Treat Maverick's tool calling performance as unverified in our suite.

Where they tie:

• Structured output (both 4/5), creative problem solving (both 3/5), faithfulness (both 4/5), safety calibration (both 2/5 — below the median for both), and persona consistency (both 5/5, tied for 1st with 36 other models). Neither model distinguishes itself on safety calibration, which sits below the 75th percentile for the broader model pool.

One Maverick note: its 1,048,576-token context window dwarfs Mistral's 131,072 tokens. If your application genuinely requires processing extremely long documents in a single pass, that architectural difference is worth considering — even though Mistral's retrieval accuracy scores higher at the 30K+ range we tested.

Llama 4 Maverick costs $0.15/M input tokens and $0.60/M output tokens. Mistral Medium 3.1 costs $0.40/M input and $2.00/M output — 2.7× more on input and 3.3× more on output. At 1M output tokens/month, that's $0.60 vs $2.00 — a $1.40 difference that's negligible. At 10M output tokens, it's $6 vs $20 — a $14/month gap, still manageable. At 100M output tokens, the gap becomes $60 vs $200 — a $140/month difference that starts to matter for cost-sensitive APIs or consumer products. For enterprises running multi-billion-token pipelines, the cost differential is substantial. Developers building high-throughput agents, document processors, or classification pipelines at scale should weigh whether Mistral Medium 3.1's benchmark advantages justify the 3.3× output cost premium.

Choose Mistral Medium 3.1 if you're building agentic workflows, analytical pipelines, document classification systems, or content editing tools where quality on strategic analysis (5 vs 2), agentic planning (5 vs 3), constrained rewriting (5 vs 3), and long-context retrieval (5 vs 4) justifies the 3.3× output cost premium. It's also the safer choice for multilingual products and tool-calling integrations given Maverick's unverified tool calling score.

Choose Llama 4 Maverick if cost is a primary constraint and your use case concentrates on persona-consistent chat, faithfulness to source material, or structured output — where both models score equivalently. Its 1M+ token context window also makes it worth evaluating for applications that need to ingest extremely large documents in a single pass, a capability Mistral's 131K window can't match. At $0.60/M output tokens, it's one of the more affordable multimodal options in our dataset.