Llama 4 Scout vs o3

Summary of test-by-test results (our 12-test suite): • Structured output: o3 5 vs Scout 4 — o3 wins and ranks tied for 1st on structured output among 54 models, so it is more reliable for strict JSON/schema outputs. • Strategic analysis: o3 5 vs Scout 2 — o3 wins and is tied for 1st (nuanced tradeoffs), while Scout ranks 44 of 54; expect better numeric tradeoff reasoning from o3. • Constrained rewriting: o3 4 vs Scout 3 — o3 wins; better at tight character-limited compression. • Creative problem solving: o3 4 vs Scout 3 — o3 wins; more effective at non-obvious, feasible ideas. • Tool calling: o3 5 vs Scout 4 — o3 wins and is tied for 1st among 54, meaning better function selection, argument accuracy, and sequencing in agentic flows. • Faithfulness: o3 5 vs Scout 4 — o3 wins and is tied for 1st among 55 models; better at sticking to source material. • Persona consistency: o3 5 vs Scout 3 — o3 wins and is tied for 1st, so it better maintains character and resists injection. • Agentic planning: o3 5 vs Scout 2 — o3 wins and is tied for 1st; stronger at goal decomposition and failure recovery. • Multilingual: o3 5 vs Scout 4 — o3 wins and is tied for 1st across 55 models. • Classification: Scout 4 vs o3 3 — Scout wins and is tied for 1st with many models; better for routing and categorization in our tests. • Long context: Scout 5 vs o3 4 — Scout wins and is tied for 1st on long context in our suite; combined with Scout’s larger context_window (327,680 vs o3’s 200,000) this benefits retrieval across very large documents. • Safety calibration: Scout 2 vs o3 1 — Scout wins (ranks 12 of 55 vs o3 rank 32); in our testing Scout refuses harmful requests more accurately while allowing legitimate ones more consistently. External benchmarks (Epoch AI): o3 scores 62.3% on SWE-bench Verified, 97.8% on MATH Level 5, and 83.9% on AIME 2025 — we cite these as supplementary evidence that o3 is strong on coding/math reasoning. Note: these numerical comparisons are from our tests and the listed external results (Epoch AI) where present.

Per-token pricing from the payload: Llama 4 Scout charges $0.08 per mTok input and $0.30 per mTok output; o3 charges $2 per mTok input and $8 per mTok output. That gap scales quickly. Using a simple 50/50 input:output assumption: • 1M tokens/month → Scout ≈ $190, o3 ≈ $5,000. • 10M tokens/month → Scout ≈ $1,900, o3 ≈ $50,000. • 100M tokens/month → Scout ≈ $19,000, o3 ≈ $500,000. Scout therefore costs roughly 3.75% of o3 at these sample volumes (priceRatio = 0.0375). Who should care: SaaS products, streaming services, or analytics platforms at 10M+ tokens/month will see six-figure differences quickly and should evaluate Scout for cost-constrained inference; teams that require the top scores in tool calling, planning, and faithfulness may justify o3's higher price.

Choose Llama 4 Scout if: you need a massive context window (327,680 tokens), best-in-class long-context retrieval, competitive classification, stricter safety calibration, or you operate at high token volumes where cost is the dominant factor (Scout costs $0.08 input / $0.30 output). Choose o3 if: you need the highest-quality structured outputs, tool calling, agentic planning, faithfulness, multilingual performance, or top math/coding results (o3 wins 9 of 12 benchmarks and posts strong third-party math/coding scores). If budget is tight and workloads are high-volume and mostly classification/long-context, pick Scout; if correctness of multi-step reasoning, tool integrations, and faithfulness matter more than cost, pick o3.