R1 0528 vs Ministral 3 14B 2512

Across our 12-test suite, R1 0528 wins six tests, wins none for Ministral, and ties the remainder (winLossTie data). Key head-to-heads: - Tool calling: R1 5 vs Ministral 4. In our rankings R1 is tied for 1st of 54 (tied with 16 others), while Ministral ranks 18 of 54. This means R1 is more reliable at function selection, argument accuracy, and sequencing for agentic flows. - Faithfulness: R1 5 vs 4 — R1 ties for 1st of 55; Ministral ranks 34 of 55. Expect R1 to stick to source content and hallucinate less in factual tasks. - Long context: R1 5 vs 4 — R1 tied for 1st of 55, Ministral ranks 38 of 55. R1 performs better on retrieval/accuracy at 30K+ tokens despite Ministral having a larger raw context window (262,144 vs 163,840). - Agentic planning: R1 5 vs 3 — R1 tied for 1st of 54; Ministral ranks 42 of 54. For goal decomposition and recovery, R1 is substantially stronger. - Safety calibration: R1 4 vs 1 — R1 ranks 6 of 55 (4 models share this score); Ministral ranks 32 of 55. R1 is significantly better at refusing harmful requests while permitting legitimate ones. - Multilingual: R1 5 vs 4 — R1 tied for 1st of 55; Ministral ranks 36 of 55. R1 gives more consistent non-English quality. Ties: structured_output (4/4), strategic_analysis (4/4), constrained_rewriting (4/4), creative_problem_solving (4/4), classification (4/4), and persona_consistency (5/5). Practical implications: choose R1 where correctness, safe refusals, long-context accuracy, and reliable tool flows matter (production assistants, automation, retrieval-augmented systems). Choose Ministral if you prioritize cost, multimodal inputs (it supports text+image->text), and a large nominal context window but can accept its weaker safety, faithfulness, and agentic planning performance. Additional external benchmarks: R1 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI); Ministral has no external math scores in the payload.

We used the payload unit prices (input_cost_per_mtok / output_cost_per_mtok) and show a 10.75x output-cost gap (priceRatio: 10.75). Using a simple 50/50 input/output token split as a practical example: - For 1M total tokens (0.5M input + 0.5M output): R1 0528 costs $1.325 (0.5*$0.5 + 0.5*$2.15); Ministral 3 14B 2512 costs $0.20 (0.5*$0.2 + 0.5*$0.2). - For 10M tokens: R1 ≈ $13.25; Ministral ≈ $2.00. - For 100M tokens: R1 ≈ $132.50; Ministral ≈ $20.00. At high volumes the gap scales linearly — switching from R1 to Ministral saves roughly $112.50 per 100M tokens under the 50/50 assumption. Who should care: small projects or high-throughput inference (apps, APIs, large-scale batch inference) will feel the difference immediately; teams that need state-of-the-art tool-calling, long-context reliability, or stricter safety/faithfulness may justify R1’s higher cost.

Choose R1 0528 if: - You need robust tool calling, agentic planning, and long-context accuracy (R1 scores 5 on those tests and is tied for 1st in our rankings). - You require stronger safety calibration and faithfulness for production assistants or regulated content. - You can absorb higher inference costs (output $2.15/mTok). Choose Ministral 3 14B 2512 if: - Cost is the primary constraint — it costs $0.20/mTok output and yields ~10.75x lower output cost than R1. - You need multimodal inputs (text+image->text in the payload) or a larger raw context window (262,144 tokens) and can accept weaker agentic planning, faithfulness, and safety calibration. - You’re building prototypes, high-volume low-cost services, or are willing to add post-processing/guardrails to mitigate lower safety/faithfulness.