GPT-4o-mini vs Ministral 3 3B 2512

Summary of our 12-test comparison (scores are from our testing):

• Ministral 3 3B 2512 wins three tests outright: faithfulness 5 vs GPT-4o-mini 3 (Ministral tied for 1st of 55 on faithfulness), constrained rewriting 5 vs 3 (Ministral tied for 1st of 53), and creative problem solving 3 vs 2 (Ministral ranks 30 of 54 vs GPT-4o-mini 47 of 54). These wins imply Ministral is better at sticking to source material, compressing/rewriting within strict limits, and producing more feasible creative ideas in our suite.
• GPT-4o-mini wins safety calibration 4 vs 1 (rank 6 of 55 in our testing), meaning it more reliably refuses harmful requests and permits legitimate ones in our prompts.
• Ties (both score 4 or equal): structured output (both 4), tool calling (both 4, rank 18 of 54 for each), classification (both 4; GPT-4o-mini is tied for 1st with 29 others), long context (both 4), persona consistency (both 4), agentic planning (both 3), multilingual (both 4), and strategic analysis (both 2). Practically, that means both models are comparable for JSON/schema adherence, function selection/sequencing, routing/classification, and handling >30K token contexts in our tests.
• Additional task scores: GPT-4o-mini posts MATH Level 5 52.6 and AIME 2025 6.9 in our testing (ranks 13/14 and 21/23 respectively), indicating limited performance on those specific competition-math items.
• Context & API surface: GPT-4o-mini has a 128,000 token window and supports text+image+file->text plus extra params (e.g., web_search_options); Ministral 3 3B 2512 has a 131,072 token window and supports text+image->text. Both have similar tool calling and long-context rankings, but their strengths diverge where faithfulness and constrained rewriting matter (Ministral) versus safety calibration (GPT-4o-mini).

Per the payload, GPT-4o-mini charges $0.60 per output mTok and $0.15 per input mTok; Ministral 3 3B 2512 charges $0.10 per output mTok and $0.10 per input mTok. For output-only volume: 1M output tokens = 1,000 mTok → GPT-4o-mini $600 vs Ministral $100; 10M → $6,000 vs $1,000; 100M → $60,000 vs $10,000. If you pay for symmetric input+output tokens (1:1): per 1M combined tokens GPT-4o-mini = $750 ($150 input + $600 output) vs Ministral = $200 ($100 + $100); 10M → $7,500 vs $2,000; 100M → $75,000 vs $20,000. Who should care: teams with high-volume inference (10M+ tokens/month) will see six-fold differences in output spend — choose Ministral for aggressive cost control. If safety calibration or specific OpenAI tooling/params matter enough to justify higher spend, GPT-4o-mini may be worth it for smaller volumes.

Choose Ministral 3 3B 2512 if you need high faithfulness, best-in-class constrained rewriting, better creative problem solving scores in our tests, and much lower inference spend (output $0.10/mTok). Choose GPT-4o-mini if safety calibration is critical (score 4 vs 1) or you require OpenAI's extended parameter set, broader modality support (file inputs), and are willing to pay ~6x more per output token for those tradeoffs.