Llama 4 Scout vs R1 Distill Llama 70B
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 4 Scout and R1 Distill Llama 70B.
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...
R1 Distill Llama 70B
DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...
Technical Specifications
| Specification | Llama 4 Scout | R1 Distill Llama 70B |
|---|---|---|
| Provider | Meta | DeepSeek |
| Context Window | 10,000,000 tokens | 128,000 tokens |
| Agent Suitability | 82/100 | N/A |
| Time to First Token (TTFT) | 350 ms | N/A |
| Deployment Model | self hostable | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-04-05 | 2025-01-23 |
API Pricing Comparison
Input Price per Million Tokens
Llama 4 Scout
$0.10
R1 Distill Llama 70B
$0.80
Output Price per Million Tokens
Llama 4 Scout
$0.30
R1 Distill Llama 70B
$0.80
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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 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling
R1 Distill Llama 70B Quirks & Gotchas
No developer gotchas reported.