Llama 3.1 8B vs Llama 4 Scout
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 3.1 8B and Llama 4 Scout.
Llama 3.1 8B
Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.
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...
Technical Specifications
| Specification | Llama 3.1 8B | Llama 4 Scout |
|---|---|---|
| Provider | Meta | Meta |
| Context Window | 131,072 tokens | 10,000,000 tokens |
| Agent Suitability | 74/100 | 82/100 |
| Time to First Token (TTFT) | 80 ms | 350 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2024-07-23 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Llama 3.1 8B
$0.04
Llama 4 Scout
$0.10
Output Price per Million Tokens
Llama 3.1 8B
$0.04
Llama 4 Scout
$0.30
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Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
Llama 3.1 8B Quirks & Gotchas
- ▸Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
- ▸Limited tool calling vs larger models — best for simple classification and chat
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling