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DeepSeek
DeepSeek V4 Pro
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Llama-3.1 8B (Instruct)
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
DeepSeek V4 ProOOM / Exceeds Limit
VRAM Footprint (Q4):1037.31 GB
Active GPU Load:0 GB
Llama-3.1 8B (Instruct)100% GPU VRAM (Safe)
VRAM Footprint (Q4):5.49 GB
Active GPU Load:5.49 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | DeepSeek V4 Pro | Llama-3.1 8B (Instruct) |
|---|---|---|
| Parameter Scale | 1600B Parameters | 8B Parameters |
| Attention Architecture | Standard GQA / MHA | Standard GQA / MHA |
| Max Context Window | 1.05M | 131.07k |
| Available GGUF Quants | 0 Quant Formats | 2 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
DeepSeek V4 Pro52.1%
Llama-3.1 8B (Instruct)72.6%
Reasoning Benchmark (GPQA / MMLU)
DeepSeek V4 Pro88%
Llama-3.1 8B (Instruct)30.4%
Frequently Asked Questions about DeepSeek V4 Pro vs Llama-3.1 8B (Instruct)
Llama-3.1 8B (Instruct) is cheaper than DeepSeek V4 Pro. Llama-3.1 8B (Instruct) has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than DeepSeek V4 Pro at $NaN/1M tokens.
For coding tasks, DeepSeek V4 Pro scores 52.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Llama-3.1 8B (Instruct) scores 72.6% on human-eval (basic standalone code completion and simple functions).
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