Llama 4 Scout vs Ministral 3 3B 2512

We ran 12 tests in our suite and compare each score below (our testing):

• Long context: Llama 4 Scout 5 vs Ministral 4 — Scout wins; Scout is tied for 1st on long-context (tied with 36 others), so it's the better pick for retrieval or summarization across 30K+ tokens.
• Safety calibration: Scout 2 vs Ministral 1 — Scout wins; Scout ranks 12 of 55 here vs Ministral 32, meaning Scout is more likely in our testing to refuse harmful prompts while allowing legitimate ones.
• Faithfulness: Scout 4 vs Ministral 5 — Ministral wins and is tied for 1st with 32 models on faithfulness, so it better sticks to source material in our tests.
• Constrained rewriting: Scout 3 vs Ministral 5 — Ministral wins (tied for 1st). This matters when you must compress or strictly fit character/byte limits.
• Persona consistency: Scout 3 vs Ministral 4 — Ministral wins; Scout ranks 45/53 while Ministral ranks 38/53, so Ministral better maintains character and resists injection in our testing.
• Agentic planning: Scout 2 vs Ministral 3 — Ministral wins; Scout ranks near the bottom (53/54) while Ministral is mid-low (42/54), so Ministral handles goal decomposition and recovery better in our tests.
• Structured output: 4 vs 4 — tie; both rank 26/54 (27 models share this score); both handle JSON/schema-style outputs similarly in our testing.
• Strategic analysis: 2 vs 2 — tie; both performed weakly on nuanced numeric tradeoffs in our testing (rank 44/54).
• Creative problem solving: 3 vs 3 — tie; both are middle-tier for non-obvious feasible ideas (rank 30/54).
• Tool calling: 4 vs 4 — tie; both rank 18/54, so function selection and argument accuracy are comparable in our testing.
• Classification: 4 vs 4 — tie; both tied for 1st with many models, indicating strong routing/categorization behavior.
• Multilingual: 4 vs 4 — tie; both rank 36/55, providing similar non-English quality. Overall: Ministral 3 3B 2512 wins 4 benchmarks (faithfulness, constrained rewriting, persona consistency, agentic planning) vs Llama 4 Scout's 2 (long context, safety); 6 tests tie. These outcomes align with the models' ranks in our dataset and indicate Ministral is stronger on fidelity and constrained outputs while Scout is the safer, long-context choice in our testing.

Per the payload, Llama 4 Scout charges $0.08 per 1M input tokens and $0.30 per 1M output tokens; Ministral 3 3B 2512 charges $0.10 per 1M input and $0.10 per 1M output. Practical examples: for 1M tokens (50/50 input/output) Scout costs $0.19 vs Ministral $0.10; for 10M tokens Scout costs $1.90 vs Ministral $1.00; for 100M tokens Scout costs $19.00 vs Ministral $10.00. Teams processing tens to hundreds of millions of tokens/month (chat platforms, high-throughput APIs) should care: Ministral cuts bill roughly in half at scale under a 50/50 I/O mix. If your workload is output-heavy, Scout becomes relatively more expensive because its $0.30 output rate is triple Ministral's $0.10 output rate.

Choose Llama 4 Scout if you need maximum long-context (327,680 token window) and stronger safety calibration in our testing—examples: multi-document retrieval, meeting-transcript consolidation, or tools that must refuse harmful input. Choose Ministral 3 3B 2512 if you prioritize factual fidelity, tight constrained rewriting, persona stability, and lower per-token cost—examples: high-volume API services, character-based assistants, and aggressive length-constrained content transforms.