Meta: Llama 4 Scout: multimodal specs, pricing, and fit
Meta: Llama 4 Scout ranks #3 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 (#3): 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 ($0.34). 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 Z.ai: GLM 5.3 Flash (1.3M ctx, $0.33/1M blended). Side by side, Meta: Llama 4 Scout offers 1.3M ctx at $0.20/1M blended: context is similar 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.