Llama 4 Scout vs Mistral Medium 3.1

Across our 12-test suite, Mistral Medium 3.1 wins 5 categories in our testing: multilingual (B 5 vs A 4), strategic analysis (5 vs 2), constrained rewriting (5 vs 3), agentic planning (5 vs 2), and persona consistency (5 vs 3). Key rank context from our rankings: multilingual for Mistral is "tied for 1st with 34 other models out of 55 tested" while Llama 4 Scout ranks 36 of 55 (18 models share its score). Strategic analysis and agentic planning are both 1st-tier ranks for Mistral (tied for 1st in their respective pools) while Llama ranks near the bottom on agentic planning (rank 53 of 54). Ties (no clear winner) appear in structured output (4 vs 4), creative problem solving (3 vs 3), tool calling (4 vs 4), faithfulness (4 vs 4), classification (4 vs 4), long context (5 vs 5), and safety calibration (2 vs 2). Practical interpretation: Mistral’s 5/5 scores and top ranks indicate it will handle non-English output, complex planning/decomposition, and tight rewriting/compression tasks more reliably in our tests. The two models are equal on core engineering-oriented checks (structured output, tool calling, classification) and both score 5 on long-context retrieval tests — but Llama 4 Scout provides a much larger context window (327,680 tokens vs Mistral’s 131,072), which is material for dense retrieval or very long-document workflows even though both scored 5 on our long context benchmark. Also note Mistral exposes runtime controls listed in the payload (temperature, stop, structured outputs, tools, etc.), which can matter for production tuning.

Raw per-1k-token pricing from the payload: Llama 4 Scout output $0.30/1k and input $0.08/1k; Mistral Medium 3.1 output $2.00/1k and input $0.40/1k. Llama is 15% of Mistral’s per-token output cost (priceRatio = 0.15). Output-only monthly cost examples: 1M tokens → Llama $300 vs Mistral $2,000; 10M → Llama $3,000 vs Mistral $20,000; 100M → Llama $30,000 vs Mistral $200,000. If you count equal input and output volume, combine input+output: 1M tokens → Llama $380 vs Mistral $2,400; 10M → $3,800 vs $24,000; 100M → $38,000 vs $240,000. Who should care: startups, high-volume apps, or any deployment doing millions of tokens/month will see large absolute savings with Llama 4 Scout; teams that need the specific benchmark wins should budget for Mistral’s higher cost.

Choose Llama 4 Scout if: you need massive context (327,680 token window), are highly cost-sensitive (output $0.30/1k; ~15% of Mistral’s output cost), or you run very high-volume workloads where per-token savings compound. Choose Mistral Medium 3.1 if: you need better multilingual quality, stronger agentic planning and strategic analysis, or reliable constrained rewriting/persona consistency — Mistral won 5 of 12 benchmarks in our testing and ranks in the top tier for those tasks. If you need both low cost and top-tier planning/multilingual capability, test Mistral on your real prompts and compare its incremental value versus the clear cost delta.