R1 vs Mistral Small 4

Across our 12-test suite, R1 wins four categories in our testing: strategic_analysis (R1 5 vs Small 4 4; R1 tied for 1st with 25 others), constrained_rewriting (R1 4 vs Small 4 3; R1 ranks 6 of 53), creative_problem_solving (R1 5 vs Small 4 4; R1 tied for 1st with 7 others), and faithfulness (R1 5 vs Small 4 4; R1 tied for 1st with 32 others). Mistral Small 4 wins two categories: structured_output (Small 4 5 vs R1 4; Small 4 tied for 1st with 24 others) and safety_calibration (Small 4 2 vs R1 1; Small 4 ranks 12 of 55 while R1 ranks 32). Six tests are ties in our testing (tool_calling 4/4; classification 2/2; long_context 4/4; persona_consistency 5/5; agentic_planning 4/4; multilingual 5/5), meaning both models perform equivalently on function selection, multilingual output, persona maintenance, and long-context retrieval in our suite. Separately, on an external math benchmark, R1 scores 93.1% on MATH Level 5 (Epoch AI) and ranks 8 of 14 on that external test; R1 also posts 53.3% on AIME 2025 (Epoch AI) and ranks 17 of 23. In practice this means: choose R1 when you need stronger tradeoff reasoning, fewer hallucinations, and competitive math performance; choose Small 4 when you require strict JSON/schema adherence or a safer refusal profile at lower cost.

Pricing units in the payload are given as input_cost_per_mtok and output_cost_per_mtok; here we interpret those as dollars per 1,000 tokens (per_mtok) and show a 50/50 input/output token split for simplicity. R1 costs input $0.7 + output $2.5 per 1k tokens; Small 4 costs input $0.15 + output $0.6 per 1k. At 1M tokens/month (500k input / 500k output): R1 ≈ $1,600 (500×$0.7 + 500×$2.5), Mistral Small 4 ≈ $375 (500×$0.15 + 500×$0.6). At 10M: R1 ≈ $16,000 vs Small 4 ≈ $3,750. At 100M: R1 ≈ $160,000 vs Small 4 ≈ $37,500. Price ratio in the payload is 4.1667 — R1 is ~4.17× more expensive per token. Teams running high-volume inference (10M+ tokens/month) or serving free/low‑cost consumer tiers should care most about the gap; small projects or research evals may accept R1's cost for its quality gains.

Choose R1 if you need best-in-class reasoning and faithfulness in our tests (wins strategic_analysis, creative_problem_solving, constrained_rewriting, faithfulness) and you can absorb ~4.17× higher token costs. Use cases: decision-support dashboards, financial/legal synthesis, competitive math assistants, and content that must stick closely to source material. Choose Mistral Small 4 if cost and schema compliance matter more (wins structured_output and safety_calibration) — use cases: high-volume API serving, strict JSON output pipelines, safety-sensitive customer-facing assistants, and projects where per-token cost dominates.