R1 0528 vs Mistral Small 3.1 24B

Across our 12-test suite, R1 0528 wins 10 benchmarks, Mistral wins 0, and they tie on 2 (structured_output and long_context). Detailed comparisons (our score / their score): strategic_analysis 4 vs 3 (R1 wins); constrained_rewriting 4 vs 3 (R1); creative_problem_solving 4 vs 2 (R1); tool_calling 5 vs 1 (R1) — note Mistral's payload marks no_tool_calling; faithfulness 5 vs 4 (R1); classification 4 vs 3 (R1); safety_calibration 4 vs 1 (R1); persona_consistency 5 vs 2 (R1); agentic_planning 5 vs 3 (R1); multilingual 5 vs 4 (R1). They tie on structured_output (4 vs 4) and long_context (5 vs 5). Context from rankings: R1 is tied for 1st on persona_consistency, faithfulness, long_context, tool_calling, and agentic_planning in our ranking data (examples: "tied for 1st with 16 other models out of 54 tested" on tool_calling); Mistral ranks 53 of 54 on tool_calling and is flagged in the payload as having no tool calling capability. For math benchmarks provided in the payload, R1 scores 96.6% on MATH Level 5 and 66.4% on AIME 2025 (Epoch AI) — we reference Epoch AI for those external tests. Practically, R1's strengths mean it will better select and sequence functions, resist prompt injection, and stick to source material; Mistral's strengths are cost and multimodal input (payload shows text+image->text), with parity on long-context retrieval and structured format adherence.

Per the payload, R1 0528 charges $0.50 per mTok input and $2.15 per mTok output; Mistral Small 3.1 24B charges $0.35 input and $0.56 output. Translate to per‑million tokens: R1 input = $500/1M, output = $2,150/1M; Mistral input = $350/1M, output = $560/1M. If you assume a 50/50 split of input/output tokens, total cost per 1M tokens is ~$1,325 for R1 0528 and ~$455 for Mistral — a $870 difference per million. At 10M tokens/month that gap is ~$8,700; at 100M it's ~$87,000. The payload's output price ratio (2.15/0.56) is ~3.84x, which explains most of the cost gap. Organizations doing high-volume inference (10M+ tokens/mo) should care about Mistral's lower price; teams that need tool calling, higher safety calibration, or faithfulness should budget for R1 0528's higher cost.

Choose R1 0528 if you need reliable tool calling, safety, faithfulness, persona consistency, or higher math performance (it wins 10 of 12 benchmarks and ranks tied for 1st on tool_calling and faithfulness). Choose Mistral Small 3.1 24B if budget and multimodal inputs matter more than orchestration: it costs roughly $455 per 1M tokens (50/50 I/O) versus ~$1,325 for R1 and supports text+image->text, but it performs poorly on tool calling and safety in our tests.