Llama 4 Scout
Meta • LLM
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...
Specifications
Context Window
10.0M tokens
Input Price/1M
$0.17
Output Price/1M
$0.66
Parameters
—
Speed
116 tok/s
Latency (TTFT)
543ms
Max Output
16K tokens
Benchmarks
Llama 4 Scout results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 2.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 30.0 | 100.0 | Artificial Analysis official API |
| SciCode | 17.0 | 100.0 | — |
| AA Coding Index | 6.7 | 100.0 | Artificial Analysis official API |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 75.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 26.0 | 100.0 | — |
Math
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 84.4 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 14.0 | 100.0 | Artificial Analysis official API |
| AA Math Index | 14.0 | 100.0 | Artificial Analysis official API |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 13.5 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 75.2 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 59.0 | 100.0 | Artificial Analysis official API |
| IFBench | 40.0 | 100.0 | — |
| HLE | 4.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 15.0 | 100.0 | — |
Information
- Release date
- April 05, 2025
- Tool Calling
- ✅ Supported
- Vision
- ✅ Supported
- Audio
- ❌ Not supported
Full Analysis: Llama 4 Scout
What is Llama 4 Scout?
Llama 4 Scout is an AI model developed by Meta, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 10.0M tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
Llama 4 Scout is usage-based, priced at $0.17/1M input tokens and $0.66/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Benchmarks & Performance
Llama 4 Scout was evaluated on 15 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show solid performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
Llama 4 Scout is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, Llama 4 Scout competes directly with similarly capable models. As an open source model, it competes with Qwen (Alibaba), Mistral, and DeepSeek, as well as proprietary models like GPT, Claude, and Gemini. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is Llama 4 Scout?
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...
How much does Llama 4 Scout cost?
Llama 4 Scout costs $0.17/1M input tokens and $0.66/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does Llama 4 Scout compare with other models?
In available benchmarks, Llama 4 Scout scored: Terminal-Bench Hard: 2/100, LiveCodeBench: 30/100, SciCode: 17/100. See the full table above for a detailed comparison.
Is Llama 4 Scout open source?
Yes, Llama 4 Scout is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
What is Llama 4 Scout best for?
Llama 4 Scout excels at multimodal tasks including text and vision. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: June 01, 2026 • View methodology →