DeepSeek V3.1 Terminus 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 V3.1 Terminus and Llama 3.1 8B.
DeepSeek V3.1 Terminus
DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...
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 V3.1 Terminus | Llama 3.1 8B |
|---|---|---|
| Provider | DeepSeek | Meta |
| Context Window | 163,840 tokens | 131,072 tokens |
| Agent Suitability | N/A | 74/100 |
| Time to First Token (TTFT) | N/A | 80 ms |
| Deployment Model | self hostable | self hostable |
| Production Stability | stable | stable |
| API Available | Yes | Yes |
| Released Date | 2025-09-22 | 2024-07-23 |
API Pricing Comparison
Input Price per Million Tokens
DeepSeek V3.1 Terminus
$0.27
Llama 3.1 8B
$0.04
Output Price per Million Tokens
DeepSeek V3.1 Terminus
$0.95
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 V3.1 Terminus Quirks & Gotchas
No developer gotchas reported.
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