Meta: Llama 4 Scout Rank
Llama 4 Scout has an overall score of 69 out of 100, tied for rank 106 of 291. The published scores are price 86, context 74, parameters 51, and freshness 63. The overall score is their average. recency is not published and is left out of the average.
Data sourced from OpenRouter API and manual data collection | Last updated: Nov 3, 2026, 8:01 AM UTC
Component scores are ranked in
tied for rank 106 of 291
Component scores
| Score | Result | Published value | Source |
|---|---|---|---|
| Freshness | 63 | AI Knowledge Cutoff | |
| Recency | — | — | AI Release Date |
| Context | 74 | 1.31M tokens, output 16.4K | AI Context Window |
| Parameters | 51 | 109B total | AI Parameter Count |
| Price | 86 | $0.15 / 1M blended, $0.1 / 1M input, $0.3 / 1M output | AI Token Price |
105 listed models have a higher overall score. Among Meta models, it ranks 1 of 8.
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...
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Frequently asked questions
What is the Llama 4 Scout rank?
Llama 4 Scout has an overall score of 69 out of 100, tied for rank 106 of 291. The published scores are price 86, context 74, parameters 51, and freshness 63. The overall score is their average. recency is not published and is left out of the average.
Where does Llama 4 Scout rank?
Llama 4 Scout is tied for rank 106 of 291. 105 listed models have a higher overall score. Among Meta models, it ranks 1 of 8.
What scores make up Llama 4 Scout?
Llama 4 Scout scores price 86 (AI Token Price), context 74 (AI Context Window), parameters 51 (AI Parameter Count), and freshness 63 (AI Knowledge Cutoff). The overall score of 69 is the average of those 4.