R1 vs Mistral Medium 3.1

Summary from our 12-test suite (win/loss/tie from payload): Mistral wins 5 tests, R1 wins 2, and 5 are ties. Detailed walk-through: - R1 wins: creative_problem_solving (R1 5 vs Mistral 3) — R1 is tied for 1st in our ranking on this task while Mistral ranks 30 of 54, so R1 is the safer choice when the task expects non-obvious, specific, feasible ideas. Faithfulness (R1 5 vs Mistral 4) — R1 ties for 1st; Mistral ranks 34 of 55, meaning R1 better sticks to source material in our testing. - Mistral wins: constrained_rewriting (5 vs 4) — Mistral tied for 1st while R1 ranks 6 of 53, so Mistral is stronger at tight-character compression and strict constraints. Classification (4 vs 2) — Mistral tied for 1st (with 29 others) and R1 ranks 51 of 53, a clear advantage for routing/categorization tasks. Long_context (5 vs 4) — Mistral ties for 1st (long-context rank tied for 1st with 36 others) while R1 sits at rank 38, making Mistral preferable for 30K+ token retrieval workflows. Safety_calibration (2 vs 1) — Mistral ranks 12 of 55 vs R1 32 of 55, so Mistral better balances refusal/allow behavior in our tests. Agentic_planning (5 vs 4) — Mistral tied for 1st; R1 ranks 16 of 54, so Mistral is stronger at task decomposition and recovery. - Ties: structured_output, strategic_analysis, tool_calling, persona_consistency, multilingual — both models scored the same in our tests and share high-ranking placements in several of these categories (e.g., both tied for 1st on strategic_analysis and multilingual). - External math benchmarks in the payload: R1 scores 93.1% on MATH Level 5 (Epoch AI) and 53.3% on AIME 2025 (Epoch AI), ranking R1 8th of 14 on MATH Level 5 and 17th of 23 on AIME 2025; Mistral has no external math scores in the provided payload. - Context & features: R1 offers a 64,000-token context window and exposes reasoning tokens/parameters (payload notes uses_reasoning_tokens and requires high max_completion_tokens). Mistral provides a 131,072-token context window and supports text+image->text modality and structured_outputs. These differences explain why Mistral wins long_context and structured tasks while R1 shows strengths in math and faithfulness in our testing.

Raw per-mTok rates from the payload: R1 input $0.70 / mTok; output $2.50 / mTok. Mistral Medium 3.1 input $0.40 / mTok; output $2.00 / mTok (the payload's priceRatio = 1.25 reflects the output-rate ratio 2.5/2.0). Converted to common volumes (1 mTok = 1,000 tokens): per 1,000,000 input tokens = R1 $700, Mistral $400; per 1,000,000 output tokens = R1 $2,500, Mistral $2,000. For a realistic 50/50 input:output split across total tokens/month, per 1M total tokens costs are: R1 $1,600 and Mistral $1,200. Scale those linearly: at 10M total tokens/month R1 ≈ $16,000 vs Mistral ≈ $12,000; at 100M total tokens/month R1 ≈ $160,000 vs Mistral ≈ $120,000. Who should care: teams doing high-volume inference (millions+ tokens/month) will see six-figure differences; small-scale or latency-focused experiments may prefer R1 for its strengths despite higher cost.

Choose R1 if: - You need top-tier creative problem solving or faithfulness in our tests (R1 scores 5 on those benchmarks). - You require strong external math performance: R1 scores 93.1% on MATH Level 5 and 53.3% on AIME 2025 (Epoch AI data in the payload). - You can tolerate higher billing (R1 output $2.50/mTok) for those benefits. Choose Mistral Medium 3.1 if: - You want lower per-mTok cost (output $2.00 vs $2.50) and better economics at scale (payload shows lower input/output rates). - Your product needs classification, long-context retrieval (30K+ tokens), agentic planning, or tighter safety calibration — Mistral wins these tests in our suite. - You need multimodal input (text+image->text) or a larger context window (131,072 tokens).