Claude 3.5 Sonnet 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 Claude 3.5 Sonnet and Llama 4 Scout.
Claude 3.5 Sonnet
Claude 3.5 Sonnet was Anthropic's breakout model of 2024, widely regarded as one of the most capable commercial LLMs of its generation. It earned widespread developer praise for its exceptional coding ability, nuanced long-form writing, and reliable complex reasoning across a 200,000-token context window. Claude 3.5 Sonnet introduced computer-use capabilities, enabling it to interact with desktop environments, web browsers, and software tools autonomously. Available via API at $3/MTok for input, it continues to be a popular choice for developers who want a well-rounded, safety-aligned model with strong benchmark performance for coding, analysis, and content generation tasks.
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 | Claude 3.5 Sonnet | Llama 4 Scout |
|---|---|---|
| Provider | Anthropic | Meta |
| Context Window | 200,000 tokens | 10,000,000 tokens |
| Agent Suitability | N/A | 82/100 |
| Time to First Token (TTFT) | N/A | 350 ms |
| Deployment Model | N/A | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2024-06-21 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
Claude 3.5 Sonnet
$3.00
Llama 4 Scout
$0.10
Output Price per Million Tokens
Claude 3.5 Sonnet
$15.00
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.
Claude 3.5 Sonnet Quirks & Gotchas
No developer gotchas reported.
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling