DeepSeek V3.2 Exp vs Llama 3.1 405B
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 Exp and Llama 3.1 405B.
DeepSeek V3.2 Exp
DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
Llama 3.1 405B
Llama 3.1 405B is Meta's largest open-weight language model and one of the most capable openly available models in the world. With 405 billion parameters, it achieves performance competitive with GPT-4 and Claude Opus across benchmarks spanning general knowledge, mathematics, coding, and multilingual tasks. Llama 3.1 405B is released under Meta's custom commercial license, supporting broad use cases including deployment via major cloud providers (AWS, GCP, Azure) and self-hosted inference with multi-GPU configurations.
Technical Specifications
| Specification | DeepSeek V3.2 Exp | Llama 3.1 405B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | N/A | 90/100 |
| Time to First Token (TTFT) | N/A | 550 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-09-29 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.2 Exp
$0.27
Llama 3.1 405B
$0.80
Output Price per Million Tokens
DeepSeek V3.2 Exp
$0.41
Llama 3.1 405B
$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.
DeepSeek V3.2 Exp Quirks & Gotchas
No developer gotchas reported.
Llama 3.1 405B Quirks & Gotchas
- ▸Massive model — requires 8× A100 80GB for FP16 inference
- ▸Available via Together AI, Fireworks, and Bedrock as managed API