Claude Opus 4.7 vs Llama 4 Scout

Across our 12-test benchmark suite (scored 1–5), Claude Opus 4.7 wins 8 tests outright, ties 3, and loses 1. Llama 4 Scout wins only classification. Here's what the scores actually mean for real work:

Agentic Planning (Opus 4.7: 5, Scout: 2) — This is the widest gap in the comparison. Opus 4.7 ties for 1st among 55 tested models; Scout ranks 54th of 55. If you're building any multi-step AI agent — one that needs to decompose goals, recover from failures, and sequence actions — this gap is disqualifying for Scout. A score of 2 in agentic planning means the model struggles with the fundamentals of autonomous task execution.

Strategic Analysis (Opus 4.7: 5, Scout: 2) — Opus 4.7 ties for 1st of 55; Scout ranks 45th. For tasks like business tradeoff analysis, nuanced decision support, or complex research summarization, Opus 4.7 operates at the top of the field while Scout falls in the bottom quartile.

Tool Calling (Opus 4.7: 5, Scout: 4) — Opus 4.7 ties for 1st of 55; Scout ranks 19th. Both models handle basic tool use, but Opus 4.7's perfect score reflects better argument accuracy and sequencing — critical for production API integrations.

Creative Problem Solving (Opus 4.7: 5, Scout: 3) — Opus 4.7 ties for 1st of 55; Scout ranks 31st. For generating non-obvious, feasible ideas, Opus 4.7 is in the top tier while Scout sits at the median.

Faithfulness (Opus 4.7: 5, Scout: 4) — Opus 4.7 ties for 1st of 56; Scout ranks 35th. Opus 4.7 is more reliable at sticking to source material without hallucinating — relevant for summarization, document QA, and RAG pipelines.

Persona Consistency (Opus 4.7: 5, Scout: 3) — Opus 4.7 ties for 1st of 55; Scout ranks 47th. For chatbot or assistant products requiring stable character and injection resistance, Scout's score of 3 (ranking near the bottom) is a meaningful concern.

Constrained Rewriting (Opus 4.7: 4, Scout: 3) — Opus 4.7 ranks 6th of 55; Scout ranks 32nd. Compressing text to hard character limits is a common real-world task — Opus 4.7 handles it more reliably.

Safety Calibration (Opus 4.7: 3, Scout: 2) — Both models score below the field median here (p50 = 2), but Opus 4.7 ranks 10th of 56 versus Scout's 13th. Neither model excels, though Opus 4.7 edges ahead.

Classification (Opus 4.7: 3, Scout: 4) — Scout's only outright win. Scout ties for 1st of 54 on classification; Opus 4.7 ranks 31st. For routing, categorization, and labeling tasks, Scout is genuinely competitive — and at 83x lower output cost, this makes it a strong specialized choice for classification pipelines.

Structured Output, Long Context, Multilingual (both models tied) — Both score 4/4 on structured output (JSON compliance), 5/5 on long context (retrieval accuracy at 30K+ tokens), and 4/4 on multilingual quality. Scout's 327,680-token context window is smaller than Opus 4.7's 1,000,000-token window, though — a meaningful difference for extremely long document workflows despite the tied benchmark scores.

The pricing gap between these two models is one of the starkest in our tracked universe. Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens. Llama 4 Scout costs $0.08 per million input tokens and $0.30 per million output tokens.

At 1 million output tokens per month, that's $25 for Opus 4.7 versus $0.30 for Scout — a $24.70 difference that's easy to absorb. At 10 million output tokens, the gap becomes $250 versus $3, or roughly $247/month. At 100 million output tokens — typical for a production API serving a moderate user base — you're looking at $2,500 versus $30 per month, a $2,470 monthly difference.

Who should care: developers building cost-sensitive consumer apps, startups with tight margins, or teams running bulk classification or routing pipelines will find Scout's pricing transformative at scale. For enterprise teams where accuracy on complex reasoning directly affects business outcomes, Opus 4.7's premium may pay for itself. The break-even question isn't just about tokens — it's whether the capability gap costs more than the price gap.

Choose Claude Opus 4.7 if: You're building agentic systems, autonomous workflows, or any pipeline where the model must plan, use tools, and recover from failures — Opus 4.7's 5 vs. 2 advantage on agentic planning in our testing is decisive. It's also the right call for strategic analysis, creative ideation, and applications where persona consistency matters (customer-facing assistants, roleplay, branded chatbots). Teams processing fewer than 10 million output tokens per month may find the cost premium manageable. The 1,000,000-token context window also gives it a structural edge for very long document work.

Choose Llama 4 Scout if: Your primary workload is classification, routing, or categorization — Scout ties for 1st of 54 models on that test in our benchmarks, and at $0.30 per million output tokens, it's one of the most cost-effective classification engines in our tested set. It's also sensible for high-volume applications where structured output and long context are the core requirements (both models tied here) and where the $2,000+ monthly savings at 100 million tokens justifies accepting weaker agentic and reasoning capability.