R1 0528 vs Mistral Small 4

Summary of 12-test head-to-head in our testing: R1 0528 wins 7 tests, Mistral Small 4 wins 1, and 4 are ties. Test-by-test detail (score format = our 1–5 scale unless noted):

• Tool calling: R1 5 vs Small 4 4 — R1 tied for 1st in our rankings (“tied for 1st with 16 other models out of 54 tested”), meaning it selects correct functions, args, and sequencing at top-tier levels in our scenarios. This matters for agentic workflows and tool-integrated apps.
• Long context: R1 5 vs Small 4 4 — R1 is tied for 1st (“tied for 1st with 36 other models out of 55 tested”), so it handled 30K+ retrieval tasks better in our tests; choose R1 for retrieval, summarization, or multi-document workflows.
• Faithfulness: R1 5 vs Small 4 4 — R1 ranks tied for 1st (“tied for 1st with 32 other models out of 55 tested”), indicating fewer hallucinations on source-based tasks in our testing.
• Classification: R1 4 vs Small 4 2 — R1 is substantially better for routing, labeling, and categorization in our tests (R1 “tied for 1st with 29 other models out of 53 tested”; Small 4 ranks 51 of 53). Expect more reliable categorization with R1.
• Agentic planning: R1 5 vs Small 4 4 — R1 tied for 1st (“tied for 1st with 14 other models out of 54 tested”), showing stronger goal decomposition and failure recovery in our scenarios.
• Safety calibration: R1 4 vs Small 4 2 — R1 performs better at refusing harmful prompts while permitting legitimate ones in our tests (R1 rank 6 of 55 vs Small 4 rank 12 of 55).
• Constrained rewriting: R1 4 vs Small 4 3 — R1 wins tasks that require strict compression or character limits in our suite (R1 rank 6 of 53 vs Small 4 rank 31 of 53).
• Structured output: R1 4 vs Small 4 5 — Small 4 wins here and is tied for 1st in structured_output (“tied for 1st with 24 other models out of 54 tested”), meaning Small 4 produced more reliable JSON/schema-compliant outputs in our evaluation.
• Creative problem solving and strategic analysis: both tie at 4 (creative_problem_solving tie; strategic_analysis tie at 4) — both models are comparable for brainstorming and nuanced tradeoffs in our tests (creative_problem_solving rank 9 of 54 for both).
• Persona consistency and multilingual: ties at 5 — both models maintain persona and non-English parity strongly in our suite (each tied for 1st on persona and multilingual with many models). Supplementary external math benchmarks: beyond our internal suite, R1 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI), showing strong performance on high-tier math tasks in that external measure. Practical implications: pick R1 when tool integration, long-context retrieval, classification, or faithfulness are primary; pick Small 4 when schema/JSON fidelity or cost (and text+image inputs) are primary.

Per the payload, R1 0528 charges $0.50 per mTok input and $2.15 per mTok output; Mistral Small 4 charges $0.15 per mTok input and $0.60 per mTok output. Using 1,000,000 tokens = 1,000 mTok: R1 input = $500, R1 output = $2,150; Mistral input = $150, Mistral output = $600. For a 50/50 input-output split per 1M tokens, R1 costs $1,325 vs Mistral $375. Scale those linearly: at 10M tokens/month R1 ≈ $13,250 vs Mistral $3,750; at 100M tokens/month R1 ≈ $132,500 vs Mistral $37,500. The payload’s priceRatio is 3.5833, showing R1 is ~3.58× more expensive overall. Who should care: high-volume, cost-sensitive deployments (startups, consumer apps) should favor Mistral to reduce monthly bills; teams requiring top-tier tool calling, long-context, or faithfulness in our testing should budget for R1 despite the higher cost.

Choose R1 0528 if you need top-tier tool calling, long-context retrieval, classification accuracy, agentic planning, or stronger safety calibration in our tests and you can absorb ~3.58× higher per-token costs. Choose Mistral Small 4 if you need structured_output reliability (JSON/schema), text+image->text support, or a much lower cost profile—ideal for high-volume or budget-constrained deployments.