R1 vs Mistral Small 3.1 24B

Overview: In our 12-test suite R1 wins 8 benchmarks, Mistral wins 2, and 2 tie. All internal scores below are 'in our testing' (1–5 scale) and ranks reference the provided ranking displays. R1 wins (in our testing): strategic_analysis 5 vs 3 — R1 'tied for 1st with 25 other models out of 54 tested' while Mistral is 'rank 36 of 54'; this indicates R1 is notably better at nuanced tradeoff reasoning for real-dollar or multi-metric decisions. Constrained_rewriting 4 vs 3 — R1 ranks 'rank 6 of 53 (25 models share this score)', so R1 handles tight compression and hard limits better. Creative_problem_solving 5 vs 2 — R1 'tied for 1st' and Mistral ranks '47 of 54', implying R1 produces more original, feasible ideas. Tool_calling 4 vs 1 — R1 ranks 'rank 18 of 54' and supports tool parameters; Mistral has the 'no_tool_calling' quirk, so R1 is the clear choice for agents and function selection. Faithfulness 5 vs 4 — R1 'tied for 1st' vs Mistral 'rank 34 of 55', meaning R1 sticks to source material more reliably in our tests. Persona_consistency 5 vs 2 — R1 'tied for 1st' vs Mistral 'rank 51 of 53', so R1 resists injection and keeps character. Agentic_planning 4 vs 3 — R1 'rank 16 of 54' vs Mistral 'rank 42 of 54', so R1 better decomposes goals and recovers from failures. Multilingual 5 vs 4 — R1 'tied for 1st' while Mistral 'rank 36 of 55', so R1 gives stronger non-English parity. Mistral wins (in our testing): classification 3 vs 2 — Mistral 'rank 31 of 53' vs R1 'rank 51 of 53', so Mistral is better at straightforward routing/categorization. Long_context 5 vs 4 — Mistral is 'tied for 1st with 36 other models out of 55 tested' while R1 is 'rank 38 of 55', making Mistral superior for retrieval or QA across 30K+ tokens and beneficial for large-document tasks. Ties: structured_output 4/4 (both 'rank 26 of 54') and safety_calibration 1/1 (both 'rank 32 of 55'), meaning both models are comparable on JSON/schema adherence and both show low safety-calibration scores in our tests. External math benchmarks: according to Epoch AI, R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025; no external SWE-bench or math percentages are provided for Mistral in the payload. Practical meaning: pick R1 for agentic assistants, complex reasoning, idea generation, and faithful outputs; pick Mistral for long-context multimodal tasks and when per-token cost is a primary constraint.

Assumptions: 'mtok' = 1,000 tokens and a 50/50 split between input and output tokens. Per the payload, R1 costs $0.70 input / $2.50 output per mtok; Mistral Small 3.1 24B costs $0.35 input / $0.56 output per mtok. That yields per 1M tokens (1,000 mtok) — R1: input $700 + output $2,500 = $3,200; Mistral: input $350 + output $560 = $910. At 10M tokens/month multiply by 10 (R1 $32,000 vs Mistral $9,100). At 100M tokens/month multiply by 100 (R1 $320,000 vs Mistral $91,000). The payload's priceRatio is ~4.46 (R1 is ~4.46x more expensive overall by these per-mtok rates). Who should care: small teams and high-volume apps will see six-figure differences at scale and should prefer Mistral for cost-sensitive workloads; teams needing the higher benchmarked quality in strategic reasoning, tool-calling, and faithfulness should budget for R1's premium.

Choose R1 if you need: - Agentic workflows and tool-calling (R1 tool_calling 4 vs Mistral 1 and Mistral 'no_tool_calling' quirk). - Strong strategic reasoning and creative problem solving (R1 strategic_analysis 5, creative_problem_solving 5; R1 ranks tied for 1st). - High faithfulness, persona consistency, and multilingual parity (R1 scores 5 in each). Choose Mistral Small 3.1 24B if you need: - Lowest cost per token at scale (per-mtok $0.35/$0.56 vs R1 $0.70/$2.50). - Very long-context retrieval (Mistral long_context 5, tied for 1st). - Multimodal inputs (payload lists Mistral modality as text+image->text). Also pick Mistral when classification or document-scale QA with images matters and budget is tight.