Ministral 3 14B 2512 vs o3

Summary of our 12-test suite (scores shown are our test scores):

• o3 wins (B): structured output 5 vs 4 (o3 tied for 1st in structured output among 54 models) — means o3 is more reliable at JSON/schema outputs.
• strategic analysis: o3 5 vs Ministral 4 (o3 tied for 1st of 54) — better for nuanced numeric tradeoffs and recommendations.
• tool calling: o3 5 vs 4 (o3 tied for 1st) — better function selection, argument accuracy, and sequencing in our tests.
• faithfulness: o3 5 vs 4 (o3 tied for 1st) — fewer source departures in tasks requiring strict fidelity.
• agentic planning: o3 5 vs 3 (o3 tied for 1st) — stronger goal decomposition and recovery behavior in our scenarios.
• multilingual: o3 5 vs 4 (o3 tied for 1st) — better parity in non-English outputs in our testing. Ministral wins classification: 4 vs o3's 3 (Ministral tied for 1st among 53 models) — better routing and categorization in our tests. Ties (both models): constrained rewriting 4, creative problem solving 4, long context 4, safety calibration 1, persona consistency 5 — meaning on creativity, compression, long-context retrieval (30K+), persona, and safety both behaved similarly in our suite. Context window and generation notes: Ministral has a larger context_window (262,144 tokens) vs o3's 200,000; o3 exposes max_output_tokens = 100,000. That matters for long-document retrieval vs very long single outputs. Third-party benchmarks (supplementary, Epoch AI): o3 scores 62.3% on SWE-bench Verified, 97.8% on MATH Level 5, and 83.9% on AIME 2025 — these external numbers help explain o3's wins on math/coding and technical reasoning in our tests. Ministral has no external SWE-bench/MATH/AIME scores in the payload to compare.

Raw token pricing (input+output): Ministral 3 14B 2512 = $0.20 + $0.20 = $0.40 per million tokens. o3 = $2 + $8 = $10.00 per million tokens. At realistic throughput: 1M tokens/month => $0.40 (Ministral) vs $10.00 (o3). 10M => $4.00 vs $100.00. 100M => $40.00 vs $1,000.00. That makes o3 ~25x more expensive per token. Teams with heavy production volumes, embedded assistants, or low-margin products should prefer Ministral for cost control; teams that need the highest task accuracy or external-math/coding performance may justify o3's higher spend.

Choose Ministral 3 14B 2512 if: you need a high-capacity context window (262,144 tokens), are extremely cost-sensitive at scale (≈ $0.40 per million tokens), or you prioritize classification and tight budget for production inference. Choose o3 if: you need top-tier tool calling, faithfulness, agentic planning, structured-output reliability, multilingual parity, or superior math/coding performance (MATH Level 5 97.8% on Epoch AI) and you can absorb ~25x higher token costs.