DeepSeek R1 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 DeepSeek R1 and Llama 4 Scout.
DeepSeek R1
A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.
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 | DeepSeek R1 | Llama 4 Scout |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 163,840 tokens | 10,000,000 tokens |
| Agent Suitability | 78/100 | 82/100 |
| Time to First Token (TTFT) | 1800 ms | 350 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | beta |
| API Available | Yes | Yes |
| Released Date | 2025-01-20 | 2025-04-05 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek R1
$0.70
Llama 4 Scout
$0.10
Output Price per Million Tokens
DeepSeek R1
$2.50
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.
DeepSeek R1 Quirks & Gotchas
- ▸Reasoning model — not designed for high-frequency tool calling
- ▸Pair with a smaller model (V4 Flash) for routing and use R1 for complex reasoning only
Llama 4 Scout Quirks & Gotchas
- ▸10M context causes significant VRAM pressure — recommend 4-bit quantization
- ▸Primarily designed for RAG, not agentic tool calling