R1 0528 vs Llama 4 Scout

Overview: In our 12-test suite R1 0528 wins 9 categories, Llama 4 Scout wins 0, and they tie on 3 (structured_output, classification, long_context). Detailed walk-through: - Tool calling: R1 5 vs Scout 4. R1 is tied for 1st of 54 models on tool_calling (tied with 16 others); Scout ranks 18 of 54. This means R1 is more reliable at selecting functions, filling arguments, and sequencing calls. - Agentic planning: R1 5 vs Scout 2. R1 is tied for 1st of 54; Scout ranks 53 of 54 — R1 is far better at goal decomposition and recovery. - Persona consistency: R1 5 vs Scout 3. R1 is tied for 1st of 53 (36 others share top); Scout is 45 of 53. R1 maintains character and resists injection much better in our tests. - Faithfulness: R1 5 vs Scout 4. R1 is tied for 1st of 55; Scout sits at 34 of 55 — R1 sticks to source material more reliably. - Safety calibration: R1 4 vs Scout 2. R1 ranks 6 of 55; Scout ranks 12 — R1 better refuses harmful prompts while permitting legitimate ones. - Constrained rewriting: R1 4 vs Scout 3 (R1 rank 6 of 53; Scout 31) — R1 handles tight length/compression constraints more accurately. - Creative problem solving: R1 4 vs Scout 3 (R1 rank 9; Scout 30) — R1 produces more feasible, non-obvious ideas. - Strategic analysis: R1 4 vs Scout 2 (R1 rank 27; Scout 44) — R1 is better at nuanced tradeoffs with numbers. - Multilingual: R1 5 vs Scout 4 (R1 tied for 1st; Scout rank 36) — R1 shows higher parity across languages. Ties: structured_output 4 vs 4 (both rank 26) and classification 4 vs 4 (both tied for 1st) and long_context 5 vs 5 (both tied for 1st). Important caveat: R1’s quirks include returning empty responses on structured_output and consuming reasoning tokens on short tasks; despite the numerical tie on structured_output, R1 may need special prompt settings (it has empty_on_structured_output and needs high max completion tokens). External math benchmarks: R1 scores 96.6% on MATH Level 5 (Epoch AI) and 66.4% on AIME 2025 (Epoch AI); Llama 4 Scout has no MATH/AIME scores in the payload. Context windows & modality: R1 context 163,840 (text->text); Scout context 327,680 and supports text+image->text — Scout is better for extremely long or multimodal inputs.

Per the payload rates (prices are per mTok): R1 0528 input $0.50 / output $2.15; Llama 4 Scout input $0.08 / output $0.30. Interpreting mTok as 1,000 tokens: output-only costs per 1M tokens = R1 $2,150, Scout $300. For 10M output tokens: R1 $21,500 vs Scout $3,000; for 100M: R1 $215,000 vs Scout $30,000. If you assume a 50/50 split of input/output tokens, per 1M total tokens R1 ≈ $1,325 (0.5M input + 0.5M output) and Scout ≈ $190. The priceRatio in the payload is ~7.17×, so at high volume (10M–100M tokens/month) Scout saves tens to hundreds of thousands of dollars. Teams with heavy production traffic, consumer apps, or tight budgets should care; research or high-stakes workflows that require R1’s superior tool calling, faithfulness, and agentic planning may justify the higher cost.

Choose R1 0528 if you need best-in-class tool calling, agentic planning, persona consistency, faithfulness, or higher math performance — and you can absorb ~7.17× higher per-token cost. Specific use cases: production agent orchestration, high-stakes assistant tasks, and multilingual/faithful responses. Choose Llama 4 Scout if budget, multimodal inputs, or very large context windows matter: it costs $0.30/output per mTok vs R1’s $2.15, supports text+image inputs, and is the cost-efficient option for high-volume or document-heavy apps.