Llama 4 Scout vs Ministral 3 14B 2512

Summary of our 12-test comparison (scores shown are from our testing): Wins for Llama 4 Scout (modelA):

• long context: Scout 5 vs Ministral 4 — Scout ties for 1st in long-context ("tied for 1st with 36 other models"), meaning it's among the top performers for retrieval/accuracy across 30K+ tokens in our tests. Expect better behavior on tasks that require reading very large documents or long chat histories.
• safety calibration: Scout 2 vs Ministral 1 — Scout ranks 12 of 55 vs Ministral 32 of 55; Scout refuses harmful prompts more appropriately in our testing. Wins for Ministral 3 14B 2512 (modelB):
• persona consistency: Ministral 5 vs Scout 3 — Ministral is tied for 1st ("tied for 1st with 36 other models"), so it maintains character/role and resists prompt injection better in our tests.
• creative problem solving: Ministral 4 vs Scout 3 — Ministral ranks 9 of 54 vs Scout 30, indicating stronger non-obvious, specific idea generation in our testing.
• constrained rewriting: Ministral 4 vs Scout 3 — Ministral ranks 6 of 53 vs Scout 31, so it handles tight character/byte-limited rewrites more reliably.
• strategic analysis: Ministral 4 vs Scout 2 — Ministral ranks 27 of 54 vs Scout 44, showing better nuanced tradeoff reasoning with real numbers in our tests.
• agentic planning: Ministral 3 vs Scout 2 — Ministral ranks 42 of 54 vs Scout 53, indicating stronger goal decomposition and recovery behavior. Ties (equal scores in our testing): structured output 4/4, tool calling 4/4, faithfulness 4/4, classification 4/4, multilingual 4/4. Both models performed similarly on JSON/schema adherence, function selection/arguments, sticking to source material, routing/classification, and non-English output quality. Practical implications: choose Ministral when you need reliable persona, creativity, tight rewriting, or strategic reasoning. Choose Scout when you need extreme context window handling or a model that better balances safety refusals in our tests. Both are comparable for tool calling, structured outputs, classification and multilingual tasks.

All prices are given per mTok in the payload; 1 mTok = 1,000 tokens, so 1,000 mToks = 1,000,000 tokens. Per-mTok rates: Llama 4 Scout input $0.08, output $0.30; Ministral 3 14B 2512 input $0.20, output $0.20. Examples (per-month totals):

• 1M tokens (1,000 mToks): input-only = Scout $80 vs Ministral $200; output-only = Scout $300 vs Ministral $200; 50/50 split = Scout $190 vs Ministral $200.
• 10M tokens (10,000 mToks): input-only = Scout $800 vs Ministral $2,000; output-only = Scout $3,000 vs Ministral $2,000; 50/50 split = Scout $1,900 vs Ministral $2,000.
• 100M tokens (100,000 mToks): input-only = Scout $8,000 vs Ministral $20,000; output-only = Scout $30,000 vs Ministral $20,000; 50/50 split = Scout $19,000 vs Ministral $20,000. What this means: Llama 4 Scout is materially cheaper for input-heavy workloads (0.08 vs 0.20), but more expensive for output tokens (0.30 vs 0.20) — Scout is 1.5× the per-output mTok cost of Ministral. Teams that produce large volumes of generated text (many output tokens) should care about the higher Scout output cost; teams that send large contexts or do retrieval-heavy calls (more input tokens) can benefit from Scout's lower input price.

Choose Ministral 3 14B 2512 if you need stronger persona consistency, creative problem solving, constrained rewrites, and strategic analysis — it wins 5 of 12 benchmarks in our testing and ranks top in persona consistency and constrained rewriting. Choose Llama 4 Scout if your priority is long-context retrieval (5/5 in our testing) or slightly better safety calibration, or if your workload is input-heavy (Scout input $0.08/mTok vs Ministral $0.20/mTok). If output volume is dominant, note Scout's higher output cost ($0.30/mTok vs $0.20/mTok) may make Ministral more economical at scale.