R1 0528 vs Ministral 3 3B 2512

Overview — wins, ties, losses: In our 12-test suite R1 0528 wins 8 benchmarks, Ministral 3 3B 2512 wins 1, and 3 are ties. R1 wins strategic_analysis (4 vs 2), creative_problem_solving (4 vs 3), tool_calling (5 vs 4), long_context (5 vs 4), safety_calibration (4 vs 1), persona_consistency (5 vs 4), agentic_planning (5 vs 3), and multilingual (5 vs 4). Ministral 3 3B 2512 wins constrained_rewriting (5 vs 4). Ties are structured_output (4 vs 4), faithfulness (5 vs 5), and classification (4 vs 4). What each win means in practice:

• Tool calling: R1 scores 5 (tied for 1st of 54) vs Ministral 4 (rank 18). In our tests R1 reliably selects functions, orders calls, and fills arguments; choose R1 when accurate function selection and chaining matter.
• Long context: R1 scores 5 (tied for 1st of 55) vs Ministral 4 (rank 38). R1 performed better on retrieval/consistency across 30K+ token contexts in our suite.
• Agentic planning & strategic analysis: R1 scores 5 on agentic_planning (tied for 1st) and 4 on strategic_analysis vs Ministral 3 and 2 respectively; R1 is stronger at goal decomposition and failure recovery in our tasks.
• Safety calibration and persona consistency: R1 scored 4 and 5 vs Ministral 1 and 4; in our testing R1 made safer refusal/allow decisions and held character more tightly.
• Constrained rewriting: Ministral 3 3B 2512 wins (5 vs R1's 4). If you need tight compression into hard character limits (e.g., microcopy, token-limited payloads), Ministral performed better in our constrained-rewriting tests.
• Faithfulness and classification: both tie (faithfulness 5, classification 4) — both models matched source material and categorization tasks similarly in our tests. External math benchmarks: Beyond our internal suite, R1 0528 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI) — these external results support R1's strong math capability. Ministral 3 3B 2512 has no external math scores in the payload. Operational note: R1 has quirks in the payload — it uses reasoning tokens and may return empty responses for structured_output/constrained_rewriting/agentic_planning on short tasks unless given high max completion tokens; plan for larger max_completion_tokens when using R1 for those workflows.

Output-only cost at scale: R1 0528 output = $2.15 per 1K tokens; Ministral 3 3B 2512 output = $0.10 per 1K. For 1M output tokens/month R1 = $2,150 vs Ministral = $100. For 10M: R1 = $21,500 vs Ministral = $1,000. For 100M: R1 = $215,000 vs Ministral = $10,000. If you also pay for equal input tokens (1:1 input:output), R1 totals $2.65 per 1K -> $2,650 / $26,500 / $265,000 for 1M/10M/100M; Ministral totals $0.20 per 1K -> $200 / $2,000 / $20,000. The priceRatio in the payload is ~21.5x. Who should care: any product with millions of tokens/month (chatbots, high-throughput APIs, large-scale generation) — Ministral 3 3B 2512 substantially reduces cost. Choose R1 0528 only when its higher scores on tool calling, long context, agentic planning, or safety materially improve downstream product value enough to justify the 20x+ premium.

Choose R1 0528 if: you require top-tier tool calling, agentic planning, long-context retrieval, multilingual parity, or safety calibration (R1 wins 8 of 12 benchmarks and ties in faithfulness/classification). Accept the ~21.5x higher output cost when these capabilities materially reduce developer time, user errors, or downstream integration cost. Choose Ministral 3 3B 2512 if: you have high-volume/low-margin usage (1M–100M tokens/month) and need a low-cost model ($0.10 per 1K output tokens) that excels at constrained rewriting and basic classification/faithfulness. Prefer Ministral when tight budget or vision (text+image->text modality) matters over top agentic/tool performance.