DeepSeek V4 Pro vs Llama 3.1 8B
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 V4 Pro and Llama 3.1 8B.
DeepSeek V4 Pro
DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...
Llama 3.1 8B
Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization — making it the default choice for on-device AI applications and local prototyping.
Technical Specifications
| Specification | DeepSeek V4 Pro | Llama 3.1 8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 1,048,576 tokens | 131,072 tokens |
| Agent Suitability | 94/100 | 74/100 |
| Time to First Token (TTFT) | 280 ms | 80 ms |
| Deployment Model | managed api | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2026-04-24 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V4 Pro
$0.43
Llama 3.1 8B
$0.04
Output Price per Million Tokens
DeepSeek V4 Pro
$0.87
Llama 3.1 8B
$0.04
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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 V4 Pro Quirks & Gotchas
- ▸MoE architecture — cold-start latency on first request, use keep-alive
- ▸Best cost-performance ratio of any frontier model — strong tool calling for agentic use
Llama 3.1 8B Quirks & Gotchas
- ▸Perfect for CPU/edge deployment — runs on Raspberry Pi with quantization
- ▸Limited tool calling vs larger models — best for simple classification and chat