Llama 3.3 70B Instruct vs Ministral 3 14B 2512

Head-to-head across our 12-test suite (scores are our internal 1–5 unless noted): Wins and ties: • Ministral (B) wins 4 tests: persona consistency 5 vs Llama's 3 (B tied for 1st of 53 models), creative problem solving 4 vs 3 (B ranks 9 of 54), constrained rewriting 4 vs 3 (B ranks 6 of 53), and strategic analysis 4 vs 3 (B ranks 27 of 54). Those wins indicate B maintains character reliably, generates more and better non-obvious ideas, compresses output within hard limits, and gives stronger tradeoff reasoning. • Llama (A) wins 2 tests: long context 5 vs 4 (A tied for 1st of 55 models) and safety calibration 2 vs 1 (A ranks 12 of 55). Llama's long context win means it performed better at retrieval/accuracy across 30K+ token contexts in our tests; it also better refuses harmful vs. legitimate requests per our safety calibration test. • Ties (no clear winner): structured output 4/4, tool calling 4/4 (both rank 18 of 54), faithfulness 4/4, classification 4/4 (tied for 1st with many models), agentic planning 3/3, multilingual 4/4. Those ties mean for format adherence, function selection, sticking to source material, and basic multilingual classification, both models behave similarly in our testing. Supplemental external math benchmarks: Llama 3.3 70B Instruct scores 41.6% on MATH Level 5 and 5.1% on AIME 2025 (Epoch AI) — Ministral has no MATH/AIME scores in the payload to compare. Practical implications: choose Ministral when you need robust persona, creative ideation, or tight rewriting; choose Llama when you need the best retrieval accuracy across very long contexts or slightly stronger safety calibration. Neither model dominates tool calling or structured output in our tests.

Pricing (per 1k-token unit = mTok in the payload): Llama 3.3 70B Instruct charges input $0.10 and output $0.32 per mTok; Ministral 3 14B 2512 charges input $0.20 and output $0.20 per mTok. Assuming a balanced 50/50 input/output mix (common for chat + short generation), combined cost per 1k tokens is $0.42 for Llama and $0.40 for Ministral. At scale (50/50 split): • 1M tokens/month (1,000 mTok): Llama ≈ $420, Ministral ≈ $400. • 10M tokens/month: Llama ≈ $4,200, Ministral ≈ $4,000. • 100M tokens/month: Llama ≈ $42,000, Ministral ≈ $40,000. Practical meaning: the per-token cost gap is small for balanced workloads (≈5% higher for Llama), but Llama becomes noticeably more expensive for output-heavy tasks because its output price is $0.32/mTok vs $0.20/mTok. Teams with very large generation volumes or output-heavy pipelines should care about Llama's higher output rate; teams optimizing tight cost-per-response may prefer Ministral.

Choose Ministral 3 14B 2512 if you need: • Strong persona consistency for product-facing agents (persona consistency 5 vs 3). • Better creative problem solving (4 vs 3) and constrained rewriting (4 vs 3) for idea generation, ad copy, or tight-format outputs. • Slightly lower balanced costs (≈$400 vs $420 per 1M tokens at a 50/50 split). Choose Llama 3.3 70B Instruct if you need: • Better long-context retrieval/accuracy in our tests (long context 5 vs 4; Llama tied for 1st of 55). • Slightly stronger safety calibration in our testing. Also prefer Llama if your workload is input-heavy (it has cheaper input at $0.10/mTok) but beware higher output costs ($0.32/mTok) for generation-heavy applications.