Model Cost Profile

Meta: Llama 4 Scout

Developer: meta-llama

Pricing updated Mar 11, 2026

Input rank: #72Output rank: #86

Live Pricing

Input: $0.0800

Output: $0.3000

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Meta: Llama 4 Scout, developed by meta-llama, offers an extensive context window of 327,680 tokens, making it suitable for complex tasks such as long-form content generation and detailed data analysis. Teams leveraging this API model will find that the input pricing is set at $0.08 per million tokens, while output costs are $0.30 per million tokens, allowing for scalable budgeting based on usage. This model is ideal for applications requiring in-depth contextual understanding, such as conversational agents and advanced research tools.

๐Ÿ‘ Vision๐Ÿ”ง Tool Calling๐Ÿ“‹ Structured Output

Context Window

327,680

Tokens

Input Price / 1M

$0.0800

Prompt tokens

Output Price / 1M

$0.3000

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Meta: Llama 4 Scout Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.0800$0.1900$0.3000Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.0800

Current Output / 1M

$0.3000

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for Meta: Llama 4 Scout.

How much does Meta: Llama 4 Scout cost per 1M input tokens?

Meta: Llama 4 Scout input pricing is $0.0800 per 1M tokens based on the latest synced provider data.

How much does Meta: Llama 4 Scout cost per 1M output tokens?

Meta: Llama 4 Scout output pricing is $0.3000 per 1M tokens based on the latest synced provider data.

What context window does Meta: Llama 4 Scout support?

Meta: Llama 4 Scout supports a context window of 327,680 tokens.

How can I compare Meta: Llama 4 Scout with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.