Llama 4 Scout vs Ministral 3 8B 2512

We ran both models across our 12-test suite and report wins/ties below (all statements are 'in our testing'). Wins: Ministral 3 8B 2512 wins strategic analysis (3 vs 2), constrained rewriting (5 vs 3), persona consistency (5 vs 3) and agentic planning (3 vs 2). Notable context: constrained rewriting is tied for 1st with 4 other models on Ministral, and persona consistency is tied for 1st with 36 others — concrete evidence it holds characters and resists injection better in our tests. Llama 4 Scout wins long context (5 vs 4) and safety calibration (2 vs 1). Llama's long context score is tied for 1st with 36 other models out of 55 tested, showing superior retrieval accuracy at 30K+ tokens in our benchmarks; it also ranks higher on safety calibration (rank 12 of 55 tied) versus Ministral (rank 32 of 55 tied). Ties (no clear winner) occurred on structured output (4/4), creative problem solving (3/3), tool calling (4/4), faithfulness (4/4), classification (4/4), and multilingual (4/4). For practical tasks this means both models behave similarly for JSON/schema compliance, tool selection/sequencing, classification routing, multilingual outputs, and faithful adherence to source content. Where scores differ: choose Ministral when you need tight-compression rewriting and consistent personas or modest agentic planning; choose Llama when you require robust long-context retrieval and stricter safety gating. Rankings give additional context: Llama's agentic planning ranks near the bottom (rank 53 of 54) while Ministral's constrained rewriting is top-tier (tied for 1st), so the differences are material for those specific tasks.

Raw price points from the payload: Llama 4 Scout input $0.08 / mTok, output $0.30 / mTok; Ministral 3 8B 2512 input $0.15 / mTok, output $0.15 / mTok. Using mTok = 1,000 tokens and a 50/50 input/output split, monthly costs scale linearly: for 1M tokens/month Llama ≈ $190, Ministral ≈ $150; for 10M tokens Llama ≈ $1,900, Ministral ≈ $1,500; for 100M tokens Llama ≈ $19,000, Ministral ≈ $15,000. If your workload is output-heavy (e.g., 80% output), Llama becomes substantially more expensive: at 1M tokens Llama ≈ $256 vs Ministral $150. If your workload is input-heavy (e.g., retrieval where input tokens dominate), Llama can be cheaper (1M tokens, 80% input: Llama ≈ $124 vs Ministral $150). Engineers and operations teams with large output volumes should care most about the gap; product teams focused on long-context understanding may accept higher output spend for Llama's long-context advantage.

Choose Ministral 3 8B 2512 if you need a cost-stable model with stronger constrained rewriting, persona consistency, and slightly better strategic/agentic planning in our tests — and if your workload is output-heavy, Ministral is usually cheaper. Choose Llama 4 Scout if your primary requirement is long-context accuracy (30K+ tokens) or you need the model to be more conservative on safety calibration; be prepared for higher output costs, which matter at scale.