GPT-5.2 vs Ministral 3 3B 2512

Across our 12-test suite, GPT-5.2 wins 7 tests, Ministral 3 3B 2512 wins 1, and 4 tests tie. External benchmarks (Epoch AI) for GPT-5.2: SWE-bench Verified 73.8 and AIME 2025 96.1 — we cite Epoch AI for those scores. Test-by-test: strategic analysis — GPT-5.2 5 vs Ministral 2 (GPT-5.2 tied for 1st of 54, meaning it’s top-tier for nuanced tradeoff reasoning); creative problem solving — 5 vs 3 (GPT-5.2 clearly better at generating non-obvious, feasible ideas); long context — 5 vs 4 (GPT-5.2 tied for 1st of 55, so better at retrieval over 30K+ tokens); safety calibration — 5 vs 1 (GPT-5.2 tied for 1st of 55, so superior at refusing harmful requests and permitting legitimate ones); persona consistency — 5 vs 4 (GPT-5.2 tied for 1st of 53); agentic planning — 5 vs 3 (GPT-5.2 tied for 1st of 54). Ministral’s sole clear win is constrained rewriting — 5 vs GPT-5.2’s 4 (Ministral tied for 1st of 53), so it’s stronger when compressing or strictly reformatting within hard limits. Ties: structured output 4/4 (both rank ~26/54), tool calling 4/4 (both rank 18/54), faithfulness 5/5 (both tied for 1st), classification 4/4 (both tied for 1st). Practical meaning: GPT-5.2 is the safer, higher-performing choice for strategy, long-context retrieval, and safety-critical flows; Ministral 3 3B 2512 offers competitive structured-output and tool-calling at a fraction of the cost and is best where constrained rewriting and budget matter. On coding-specific external evidence, GPT-5.2’s 73.8 on SWE-bench Verified (Epoch AI) places it 5th of 12 in that external comparison.

Costs per thousand tokens (mTok) from the payload: GPT-5.2 input $1.75 + output $14.00 = $15.75/mTok; Ministral 3 3B 2512 input $0.10 + output $0.10 = $0.20/mTok. At real volumes (input+output combined): 1M tokens (1,000 mTok) costs $15,750 on GPT-5.2 vs $200 on Ministral; 10M tokens costs $157,500 vs $2,000; 100M tokens costs $1,575,000 vs $20,000. The priceRatio provided is 140 — GPT-5.2 is ~140× more expensive per-token. Teams doing heavy production inference, high-volume customer-facing chat, or embedding large corpora should care most about this gap; smaller projects or those prioritizing top-tier long-context reasoning may accept GPT-5.2’s premium.

Choose GPT-5.2 if you need top-tier strategic analysis, long-context retrieval, strong safety calibration, and the best AIME performance (AIME 96.1, SWE-bench 73.8) and can absorb a much higher per-token bill. Choose Ministral 3 3B 2512 if your priority is dramatic cost savings (combined $0.20/mTok) for high-volume inference, tight constrained-rewriting tasks, or when you need a small, efficient multimodal model for production at scale.