Meta: Llama 4 Scout
Analysis Summary
Meta: Llama 4 Scout sits in the Efficient tier on our leaderboard, ranked #190 of 551 published models on overall intelligence. At $0.080 input and $0.300 output per 1M tokens, it is among the most expensive on the market. It offers a generous context window for extended reasoning and code review and supports tool use, function calling, and vision.
Editorial notes
Meta Llama 4 Scout delivers vision, tool use, and a 327K context at very low pricing, with reasonable GPQA and MMLU scores, though coding and agentic benchmarks are limited.
Assessed May 5, 2026
Rankings consider pricing, capabilities, benchmarks, and real-world applicability and are refreshed as new models launch. Feedback?
Performance Profile
Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input..
Capabilities
Performance Indices
Source: Artificial Analysis
Benchmark Scores
Intelligence
Technical
Content
Benchmark data from Artificial Analysis and Hugging Face
How does Meta: Llama 4 Scout stack up?
Compare side-by-side with other efficient models.
Model Information
| OpenRouter ID |
meta-llama/llama-4-scout
|
| Provider | meta-llama |
| Model Family | Llama 4 |
| Release Date | April 5, 2025 |
| Context Length | 327,680 tokens |
| Max Completion | 16,384 tokens |
| Status | Active |
Pricing
| Token Type | Cost per 1M tokens | Cost per 1K tokens |
|---|---|---|
| Input | $0.08 | $0.000080 |
| Output | $0.30 | $0.000300 |
Live Performance
Live endpoint metrics ā refreshed every 30 minutes.
