Meta: Llama 4 Scout: multimodal specs, pricing, and fit
Meta: Llama 4 Scout ranks #2 on the AI Hippo board with a 1.3M context window and $0.10 input / $0.30 output per 1M tokens. Specs, cost math, and how it compares.
The quick read
Meta: Llama 4 Scout from meta-llama is a balanced pick (#2): a 1.3M window at $0.10 input / $0.30 output per 1M tokens. It aims for capability headroom without top-tier prices—useful when neither raw cost nor absolute frontier quality is the only constraint.
Spec sheet at a glance
By the numbers: 1.3M context window; multimodal (text, image, and file input); 15 exposed API parameters, including tool calling and structured outputs. Use these to judge fit for agentic, long-context, or structured-output workloads.
Pricing & how it compares
Pricing: $0.10 input / $0.30 output per 1M tokens. Its blended $0.20 is at or below the top-20 median ($0.66). On AI Hippo the composite board rewards cheap long-context models, so a lower rank here means weaker value-per-token, not weaker capability.
Head-to-head
Its closest board neighbor is Xiaomi: MiMo-V2.5 (1.1M ctx, $0.21/1M blended). Side by side, Meta: Llama 4 Scout offers 1.3M ctx at $0.20/1M blended: context is longer and, on blended token price, it is cheaper. Pick between them on whichever axis your workload is bound by.
Where it fits
Best-fit workloads: whole-repo & long-document work, screenshot / PDF / image understanding, tool-calling agents, structured-output pipelines, high-volume, cost-sensitive batch.
Cost in practice & verdict
A representative 100K-input + 20K-output task costs about $0.02 on Meta: Llama 4 Scout. Verdict: strong price-performance for a default, high-volume workhorse where every token counts.
Sources
Evidence and actions
- Time window: catalog snapshot
- Observation count: 20
- Source type: catalog_and_pricing
- See its board position on /en/rankings/
- Verify multi-source pricing on /en/token/
- Compare side by side on /en/compare/?m=Meta%3A%20Llama%204%20Scout