Llama 4 Maverick vs Llama 4 Scout

All benchmark claims below are from our 12-test suite. Overall wins: Llama 4 Scout wins 3 categories (tool calling, classification, long context); Llama 4 Maverick wins 2 (persona consistency, agentic planning); the remaining categories tie. Detailed walk-through: - Tool calling: Scout scores 4 in our testing and ranks 18 of 54 (tied); Maverick encountered a transient 429 rate limit during the tool calling test on OpenRouter but has no higher score here in our data — in practice Scout is stronger at function selection, argument accuracy, and sequencing. - Classification: Scout scores 4 vs Maverick 3; Scout is tied for 1st on classification (tied with 29 others out of 53), so it’s the safer pick for routing and tagging tasks. - Long context: Scout scores 5 vs Maverick 4; Scout ties for 1st on long context (tied with 36 others out of 55), meaning better retrieval accuracy at 30K+ token contexts in our tests. - Persona consistency: Maverick scores 5 vs Scout 3; Maverick is tied for 1st on persona consistency (tied with 36 others out of 53), so it maintains character and resists injection better in roleplay or persona-driven agents. - Agentic planning: Maverick 3 vs Scout 2; Maverick ranks 42 of 54 here vs Scout at 53 of 54, so Maverick is measurably better at goal decomposition and failure recovery in our testing. - Ties: structured output (4/4), strategic analysis (2/2), constrained rewriting (3/3), creative problem solving (3/3), faithfulness (4/4), safety calibration (2/2), and multilingual (4/4) — these show comparable behavior between the models on format adherence, nuanced tradeoffs, constrained rewrites, creativity, source fidelity, safety refusals, and multilingual output. Note the Maverick tool calling quirk: our test hit a transient 429 rate-limit on OpenRouter (tool calling_rate_limited true) which may have affected that run; we flag that as likely transient.

Pricing is a clear operational factor: Maverick charges $0.15 per mTok input and $0.60 per mTok output; Scout charges $0.08/$0.30 (input/output). Assuming mTok = 1,000 tokens and a 50/50 input/output split, 1M tokens/month costs $375 on Maverick vs $190 on Scout — a $185 monthly saving with Scout. At 10M tokens/month those costs scale to $3,750 vs $1,900 (save $1,850); at 100M tokens/month $37,500 vs $19,000 (save $18,500). If your workload is output-heavy, the gap widens (for 1M output-only: Maverick ≈ $600 vs Scout ≈ $300). High-volume services, startups on tight margins, and teams running many realtime sessions should care most about the Scout vs Maverick cost gap.

Choose Llama 4 Maverick if: - You need strong persona consistency (score 5, tied for 1st) or better agentic planning (3 vs Scout 2). Use cases: character-driven chatbots, roleplay assistants, or agents where robust failure recovery is critical despite higher per-token cost. Choose Llama 4 Scout if: - You need long-context retrieval (score 5, tied for 1st), robust tool calling (score 4, rank 18/54), or top-tier classification (score 4, tied for 1st) at half the price. Use cases: high-volume RAG systems, classification/routing pipelines, multi-tool orchestration, and cost-sensitive production deployments.