R1 vs Mistral Small 3.2 24B

Overview (our 12-test suite): R1 wins five tests, Mistral wins one, and six are ties. R1 wins: strategic_analysis (R1 score 5 vs Mistral 2) — R1 is tied for 1st of 54 models in this test in our testing, meaning better nuanced tradeoff reasoning for finance/strategy prompts. creative_problem_solving (R1 5 vs Mistral 2) — R1 tied for 1st of 54, implying stronger idea-generation for product design and R&D briefs. faithfulness (R1 5 vs Mistral 4) — R1 tied for 1st of 55, so fewer hallucinations when sticking to source material. persona_consistency (R1 5 vs Mistral 3) — R1 tied for 1st of 53, useful for character-driven chat or role-playing assistants. multilingual (R1 5 vs Mistral 4) — R1 tied for 1st of 55, better parity across non-English outputs. Mistral wins classification (Mistral 3 vs R1 2) — Mistral ranks 31 of 53 vs R1 at rank 51 of 53 in our testing, so Mistral is the better choice for routing/categorization tasks. Ties (both score 4): structured_output (rank 26/54), constrained_rewriting (rank 6/53), tool_calling (rank 18/54), long_context (rank 38/55), safety_calibration (rank 32/55), and agentic_planning (rank 16/54) — these indicate comparable performance for JSON/formatting, function selection, long-context retrieval, safety refusal behavior, and planning in our tests. External math benchmarks (supplementary): R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI), ranking 8/14 and 17/23 respectively — useful if high-level competition math performance matters. In short: R1 is measurably stronger on reasoning, faithfulness, creativity and multilingual tests in our testing; Mistral is cheaper and better at classification.

Per-token pricing (per mTok): R1 input $0.70, output $2.50; Mistral Small 3.2 24B input $0.075, output $0.20. At 1,000,000 tokens (1M): R1 costs — input-only $700, output-only $2,500, 50/50 mix $1,600; Mistral costs — input-only $75, output-only $200, 50/50 mix $137.50. At 10M tokens: R1 input $7,000, output $25,000, 50/50 $16,000; Mistral input $750, output $2,000, 50/50 $1,375. At 100M tokens: R1 input $70,000, output $250,000, 50/50 $160,000; Mistral input $7,500, output $20,000, 50/50 $13,750. The payload’s priceRatio is 12.5 — R1 is roughly 12.5x more costly per token. Who should care: startups or high-volume apps (10M–100M tokens/month) will see outsized spend differences and should prefer Mistral for cost efficiency; teams that need the top-tier faithfulness, multilingual and creative outputs at smaller scale or for high-value queries may justify R1’s higher price.

Choose R1 if you need top-tier faithfulness, creative problem solving, strategic reasoning, or robust multilingual outputs for high-value queries and can afford the higher per-token cost (R1 is ~12.5x more expensive). Choose Mistral Small 3.2 24B if you must minimize inference cost at scale, need better classification/routing, or run high-volume applications where the $1k–$100k/month cost delta matters.