Llama 4 Scout vs Mistral Small 4

Mistral Small 4 outperforms Llama 4 Scout on 6 of 12 tests in our suite, with 4 ties and Llama 4 Scout winning the remaining 2.

Where Mistral Small 4 wins:

• Structured output (5 vs 4): Mistral Small 4 ties for 1st among 54 models; Llama 4 Scout sits mid-pack (rank 26 of 54). For any workflow relying on strict JSON schema compliance, this is a meaningful gap.
• Strategic analysis (4 vs 2): This is the widest gap in the comparison. Mistral Small 4 ranks 27th of 54 — solidly above median — while Llama 4 Scout ranks 44th of 54 with a score of 2, well below the field median of 4. If your use case involves nuanced tradeoff reasoning or data-driven decision support, Llama 4 Scout's weakness here is disqualifying.
• Agentic planning (4 vs 2): Mistral Small 4 ranks 16th of 54; Llama 4 Scout ranks 53rd of 54 — second to last in our entire tested set. This is a critical gap for multi-step task automation or any orchestration-style deployment.
• Creative problem solving (4 vs 3): Mistral Small 4 ranks 9th of 54; Llama 4 Scout ranks 30th. Useful for ideation, brainstorming, and open-ended reasoning tasks.
• Persona consistency (5 vs 3): Mistral Small 4 ties for 1st among 53 models. Llama 4 Scout ranks 45th. Relevant for chatbot and roleplay applications that need a stable character.
• Multilingual (5 vs 4): Both score above median, but Mistral Small 4 ties for 1st among 55 models while Llama 4 Scout is tied at rank 36. For non-English deployments, Mistral Small 4 is the safer choice.

Where Llama 4 Scout wins:

• Long context (5 vs 4): Llama 4 Scout ties for 1st among 55 models with a 327,680-token context window vs Mistral Small 4's 262,144. Both are well above median, but Llama 4 Scout's combination of top score and larger window makes it the clear pick for retrieval-heavy, long-document tasks.
• Classification (4 vs 2): Llama 4 Scout ties for 1st among 53 models; Mistral Small 4 ranks 51st of 53. This is a dramatic reversal. For routing, tagging, categorization, or content moderation pipelines, Llama 4 Scout is substantially better.

Ties (4 tests): Both models score identically on tool calling (4), faithfulness (4), constrained rewriting (3), and safety calibration (2). The tool calling tie places both at rank 18 of 54 — a competitive mid-field result. The safety calibration tie at 2/5 is worth flagging: both models score below the field median of 2 at the 50th percentile, meaning they are average at best on refusing harmful requests while permitting legitimate ones.

Llama 4 Scout costs $0.08 per million input tokens and $0.30 per million output tokens. Mistral Small 4 costs $0.15 input and $0.60 output — exactly double across the board. At 1M output tokens/month, that gap is $0.30 vs $0.60 — negligible. At 10M output tokens, it's $3 vs $6 — still minor. At 100M output tokens, you're paying $30 vs $60 — a $30/month difference. At 1B tokens, the gap reaches $300/month on output alone. For hobbyists or low-volume apps, the price difference is inconsequential. For production workloads pushing hundreds of millions of tokens monthly — think customer service pipelines, content automation, or large-scale document processing — Llama 4 Scout's cost advantage becomes a real budget line item. Developers who need Mistral Small 4's superior agentic or strategic capabilities and are operating at scale should model that 2x cost explicitly before committing.

Choose Llama 4 Scout if: Your primary workload is document classification, routing, or content tagging — it ranks tied for 1st on classification (vs Mistral Small 4's 51st of 53). Also choose Llama 4 Scout if you're doing retrieval or summarization over very long documents (tied for 1st on long context, 327K token window), or if you're running high-volume production workloads where the 2x cost difference compounds materially.

Choose Mistral Small 4 if: You're building agentic systems, multi-step planners, or anything requiring goal decomposition — it ranks 16th on agentic planning while Llama 4 Scout ranks 53rd of 54. Also choose Mistral Small 4 for strategic analysis tasks (rank 27 vs 44), structured output workflows (tied for 1st), multilingual applications (tied for 1st), or persona-driven chatbots (tied for 1st on persona consistency). For most general-purpose applications, Mistral Small 4 is the stronger model — the 2x cost premium is justified unless your specific workload maps to Llama 4 Scout's two standout strengths.