Llama 4 Scout vs Mistral Large 3 2512

Across our 12-test benchmark suite, Mistral Large 3 2512 wins 5 tests, Llama 4 Scout wins 3, and they tie on 4.

Where Mistral Large 3 2512 wins:

• Structured output (5 vs 4): Mistral ties for 1st among 54 models tested; Scout ranks 26th (tied). For applications that require reliable JSON schema compliance — API integrations, data extraction pipelines — this is a meaningful edge.
• Strategic analysis (4 vs 2): This is the widest gap in the comparison. Mistral ranks 27th of 54; Scout ranks 44th of 54. Strategic analysis tests nuanced tradeoff reasoning with real numbers, and a 4 vs 2 gap means Scout underperforms substantially here — relevant for financial modeling, business analysis, or advisory tools.
• Faithfulness (5 vs 4): Mistral ties for 1st of 55; Scout ranks 34th. Faithfulness measures how well a model sticks to source material without hallucinating, which is critical for RAG and document summarization tasks.
• Agentic planning (4 vs 2): Mistral ranks 16th of 54; Scout ranks 53rd of 54 — near the bottom. Scout's 2/5 here is a significant weakness for multi-step agent workflows requiring goal decomposition and failure recovery.
• Multilingual (5 vs 4): Mistral ties for 1st of 55; Scout ranks 36th (tied). For non-English applications, Mistral delivers meaningfully more consistent output quality.

Where Llama 4 Scout wins:

• Long context (5 vs 4): Scout ties for 1st of 55 models and has the larger context window (327K vs 262K tokens). Mistral ranks 38th. For retrieval tasks at 30K+ tokens, Scout has a real advantage.
• Classification (4 vs 3): Scout ties for 1st of 53; Mistral ranks 31st. For routing, tagging, and categorization workloads, Scout performs at the top of the field while Mistral sits in the middle tier.
• Safety calibration (2 vs 1): Both models score low here relative to the field, but Scout's 2 beats Mistral's 1. Scout ranks 12th of 55; Mistral ranks 32nd. Mistral's 1/5 — the lowest score on this test — means it may over-refuse legitimate requests or under-refuse harmful ones more than most models in our testing.

Ties (both score identically):

• Tool calling: both 4/5, both rank 18th of 54
• Constrained rewriting: both 3/5, both rank 31st of 53
• Creative problem solving: both 3/5, both rank 30th of 54
• Persona consistency: both 3/5, both rank 45th of 53

The tie pattern is telling — these models perform identically on creativity, persona, rewriting, and tool use. The differentiation is concentrated in reasoning depth (strategic analysis, agentic planning) where Mistral leads, and long-context retrieval plus classification where Scout leads.

Llama 4 Scout costs $0.08/MTok input and $0.30/MTok output. Mistral Large 3 2512 costs $0.50/MTok input and $1.50/MTok output — 6.25x more on input and 5x more on output. In practice, output cost usually dominates for generative workloads. At 1M output tokens/month, Llama 4 Scout costs $0.30 vs Mistral's $1.50 — a $1.20 difference that's easy to absorb. At 10M output tokens/month, that gap grows to $12 vs $15, still manageable but increasingly meaningful. At 100M output tokens/month, you're looking at $300 vs $1,500 — a $1,200/month premium for Mistral's capabilities. Teams running high-volume classification, retrieval, or long-context tasks (where Llama 4 Scout matches or beats Mistral) have strong financial incentive to stay with Scout. Teams whose workflows depend on agentic planning, strategic analysis, or multilingual output — areas where Mistral scores measurably higher — need to decide if those quality gains justify $1,200+ per 100M output tokens.

Choose Llama 4 Scout if:

• You're running high-volume classification, routing, or tagging pipelines — it ties for 1st of 53 models on classification while Mistral ranks 31st.
• Long-context retrieval is your primary use case — Scout's 5/5 (1st of 55) and 327K context window outperform Mistral's 4/5 at 262K.
• Cost is a hard constraint — at $0.30/MTok output vs $1.50, Scout is 5x cheaper on generation, saving $1,200/month per 100M output tokens.
• You need reliable safety calibration — Scout scores 2/5 (rank 12 of 55) vs Mistral's 1/5 (rank 32 of 55).

Choose Mistral Large 3 2512 if:

• You're building agentic or multi-step workflows — Mistral's 4/5 on agentic planning (rank 16 of 54) vs Scout's 2/5 (rank 53 of 54) is a capability gap too large to ignore.
• Your application does RAG, document grounding, or any task demanding faithfulness — Mistral's 5/5 (tied 1st of 55) vs Scout's 4/5 (rank 34th) reduces hallucination risk meaningfully.
• You're serving non-English users — Mistral's 5/5 multilingual (tied 1st of 55) vs Scout's 4/5 (rank 36th) ensures more consistent output quality across languages.
• You need sophisticated analytical reasoning — Mistral's 4/5 on strategic analysis vs Scout's 2/5 is the starkest single-test gap and disqualifies Scout for complex business or financial reasoning tasks.
• You require reliable structured output for JSON-heavy integrations — Mistral ties for 1st of 54; Scout ranks 26th.