DeepSeek V3.2 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 V3.2 and Llama 3.3 70B Instruct.
DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
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 V3.2 | Llama 3.3 70B Instruct |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 131,072 tokens | 131,072 tokens |
| Agent Suitability | N/A | 83/100 |
| Time to First Token (TTFT) | N/A | 280 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-12-01 | 2024-12-06 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.2
$0.23
Llama 3.3 70B Instruct
$0.10
Output Price per Million Tokens
DeepSeek V3.2
$0.34
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 V3.2 Quirks & Gotchas
No developer gotchas reported.
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