Ministral 3 3B 2512 vs o3

Across our 12-test suite, o3 wins 7 tasks, Ministral 3 3B 2512 wins 2, and 3 are ties. Detailed walk-through (scores are our internal 1–5 unless otherwise noted):

• Tool calling: o3 5 vs Ministral 4 — in our testing o3 is more reliable at function selection, argument accuracy, and sequencing; o3’s ranking is "tied for 1st with 16 others out of 54" on tool calling.
• Strategic analysis: o3 5 vs Ministral 2 — a large gap for nuanced tradeoff reasoning (o3 tied for 1st with 25 others; Ministral ranks 44 of 54), so use o3 for multi‑step numeric tradeoffs.
• Agentic planning: o3 5 vs Ministral 3 — o3 leads on goal decomposition and failure recovery (o3 tied for 1st; Ministral rank 42 of 54).
• Creative problem solving: o3 4 vs Ministral 3 — o3 shows stronger, more specific feasible ideas (o3 rank 9 of 54; Ministral rank 30 of 54).
• Structured output (JSON/schema): o3 5 vs Ministral 4 — o3 is better at schema adherence (o3 tied for 1st; Ministral rank 26 of 54), useful for API integrations and data pipelines.
• Persona consistency: o3 5 vs Ministral 4 — o3 maintains character and resists injection better (o3 tied for 1st; Ministral rank 38 of 53).
• Multilingual: o3 5 vs Ministral 4 — o3 produces higher quality non‑English output in our tests (o3 tied for 1st; Ministral rank 36 of 55).
• Constrained rewriting: Ministral 5 vs o3 4 — Ministral is stronger at tight character/byte limits (Ministral tied for 1st; o3 rank 6 of 53).
• Classification: Ministral 4 vs o3 3 — Ministral tied for 1st with many models on routing and categorization; o3 ranks 31 of 53 here.
• Faithfulness: tie 5 vs 5 — both models score top marks for sticking to source material (both tied for 1st with many models).
• Long context: tie 4 vs 4 — both handle 30K+ token retrieval comparably (both rank 38 of 55).
• Safety calibration: tie 1 vs 1 — both share the same refusal/allow behavior in our tests (rank 32 of 55). External benchmarks (supplementary, Epoch AI): o3 scores 62.3% on SWE‑bench Verified, 97.8% on MATH Level 5, and 83.9% on AIME 2025 (Epoch AI). These external results reinforce o3’s lead on coding and math tasks; Ministral 3 3B 2512 has no external scores in the payload to compare directly. All internal benchmark claims above are phrased as "in our testing."

Ministral 3 3B 2512: input $0.10 per M tokens, output $0.10 per M. o3: input $2 per M, output $8 per M. Using a typical 50/50 input/output split, cost per million tokens is $0.10 for Ministral 3 3B 2512 and $5.00 for o3. At 1M tokens/month that’s $0.10 vs $5.00; at 10M it’s $1.00 vs $50.00; at 100M it’s $10.00 vs $500.00. If your workload is output‑heavy (more generated tokens than prompts), o3’s $8/M output price amplifies the gap. Enterprises, high‑volume SaaS, and any application doing >10M tokens/month should model these dollars precisely—Ministral 3 3B 2512 materially reduces operating cost, while o3 demands significantly higher budget for better benchmark performance.

Choose Ministral 3 3B 2512 if: you need the cheapest inference for high‑volume rules engines, constrained rewriting (it scores 5/5 and is tied for 1st), or low‑cost classification tasks — expect $0.10 per million tokens at a 50/50 split. Choose o3 if: you prioritize developer productivity, tool calling, strategic analysis, multilingual output, structured schemas, or math/coding accuracy — it wins 7 of 12 internal benchmarks and posts strong external scores on SWE‑bench and MATH Level 5 (Epoch AI), but plan for significantly higher cost (input $2/M, output $8/M).