R1 vs Llama 4 Scout

We ran our 12-test suite and R1 wins the majority (7 wins), Llama 4 Scout wins 3 tests, and 2 are ties. Test-by-test (scores are from our tests):

• Strategic analysis: R1 5 vs Llama 4 Scout 2. R1’s 5 is tied for 1st in our ranking (tied with 25 others out of 54), so expect stronger nuanced tradeoff reasoning with R1. Llama’s 2 places it near the bottom (rank 44/54).
• Constrained rewriting: R1 4 vs Scout 3. R1 ranks 6/53 (25 models share the score) — better for hard-length compression and tight editing.
• Creative problem solving: R1 5 vs Scout 3. R1 is tied for 1st (tied with 7 others), so it generates more non-obvious, feasible ideas in our tests.
• Faithfulness: R1 5 vs Scout 4. R1 is tied for 1st (tied with 32 others out of 55), meaning it sticks to source material more reliably in our suite; Scout’s 4 ranks 34/55.
• Persona consistency: R1 5 vs Scout 3. R1 is tied for 1st (tied with 36 others out of 53) — better at maintaining tone and resisting injection attacks.
• Agentic planning: R1 4 vs Scout 2. R1 ranks 16/54 (stronger goal decomposition and recovery), while Scout ranks 53/54.
• Multilingual: R1 5 vs Scout 4. R1 ties for 1st (tied with 34 others out of 55) — better non-English parity in our tests.
• Classification: R1 2 vs Scout 4. Llama 4 Scout is tied for 1st with 29 other models out of 53 — choose Scout when routing or classification is critical.
• Long context: R1 4 vs Scout 5. Scout is tied for 1st with 36 other models out of 55; Scout also offers a much larger context window (327,680 tokens vs R1’s 64,000) — practical advantage for extremely long documents or codebases.
• Safety calibration: R1 1 vs Scout 2. Scout ranks 12/55 on safety calibration while R1 ranks 32/55 — Scout better balances refusal of harmful requests vs permitting legitimate ones in our tests.
• Structured output: R1 4 vs Scout 4 — tie (both rank 26/54) meaning similar JSON/schema adherence in our tests.
• Tool calling: R1 4 vs Scout 4 — tie (both rank 18/54) indicating comparable function selection and argument accuracy in our suite. Additional math signals (external benchmarks): R1 scores 93.1 on MATH Level 5 and 53.3 on AIME 2025 (according to Epoch AI); R1’s MATH Level 5 ranks 8/14 and AIME 2025 ranks 17/23 in our dataset. Llama 4 Scout has no MATH/AIME scores in the payload. Together this shows R1 is stronger on multi-step reasoning and math-style problems in our tests, while Scout’s strengths are long-context and classification.

R1 is materially more expensive: input $0.7/mtok and output $2.5/mtok versus Llama 4 Scout at $0.08/mtok input and $0.3/mtok output (priceRatio 8.33). At a realistic 50/50 input/output split per million tokens: R1 costs $1,600 per 1M tokens ($700 input + $2,500 output per 1M scaled to 50/50 → $350+$1,250=$1,600); Llama 4 Scout costs $190 per 1M ($80 input + $300 output per 1M scaled to 50/50 → $40+$150=$190). Scale those linearly: at 10M tokens/month R1 ≈ $16,000 vs Llama 4 Scout ≈ $1,900; at 100M tokens/month R1 ≈ $160,000 vs Llama 4 Scout ≈ $19,000. Teams with heavy production traffic or limited budgets should care: the cost gap becomes tens to hundreds of thousands of dollars at scale. Single-user prototyping or low-volume apps may accept R1’s premium for its stronger reasoning and faithfulness, but high-volume deployments should evaluate Llama 4 Scout for cost-sensitive throughput.

Choose R1 if: you need the strongest multi-step reasoning, creative problem solving, faithfulness to source, multilingual quality, or persona consistency — and you can absorb a significantly higher per-token cost. R1 won 7 of 12 benchmarks in our tests and posts top-tier ranks on strategic analysis and faithfulness. Choose Llama 4 Scout if: you need a dramatically lower-cost engine for high-volume inference, the largest context windows (327,680 tokens) for long documents or codebases, or best-in-class classification and safer refusals in our tests. Scout won long-context, classification, and safety calibration and costs ~8.33× less per token.