DeepSeek R1 vs Llama 3.3 70B Instruct
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 3.3 70B Instruct.
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 3.3 70B Instruct
Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.
Technical Specifications
| Specification | DeepSeek R1 | Llama 3.3 70B Instruct |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | 78/100 | 83/100 |
| Time to First Token (TTFT) | 1800 ms | 280 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-01-20 | 2024-12-06 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek R1
$0.70
Llama 3.3 70B Instruct
$0.10
Output Price per Million Tokens
DeepSeek R1
$2.50
Llama 3.3 70B Instruct
$0.32
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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 3.3 70B Instruct Quirks & Gotchas
- ▸Stable, well-documented self-hosted option with strong community support
- ▸Outperformed by Llama 4 Maverick for agentic tool-calling workflows