Back to SearchHardware Shootout
DeepSeek
DeepSeek V4 Flash
VS
Meta
Llama-3.3-70B
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
DeepSeek V4 FlashOOM / Exceeds Limit
VRAM Footprint (Q4):145.97 GB
Active GPU Load:12.61 GB
Llama-3.3-70BPartial CPU Offload
VRAM Footprint (Q4):42.11 GB
Active GPU Load:14.18 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | DeepSeek V4 Flash | Llama-3.3-70B |
|---|---|---|
| Parameter Scale | 284B Parameters | 70B Parameters |
| Attention Architecture | Standard GQA / MHA | Standard GQA / MHA |
| Max Context Window | 1.05M | 131.07k |
| Available GGUF Quants | 2 Quant Formats | 2 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
DeepSeek V4 Flash49.1%
Llama-3.3-70B88.4%
Reasoning Benchmark (GPQA / MMLU)
DeepSeek V4 Flash80%
Llama-3.3-70B50.5%
Frequently Asked Questions about DeepSeek V4 Flash vs Llama-3.3-70B
Llama-3.3-70B is cheaper than DeepSeek V4 Flash. Llama-3.3-70B has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than DeepSeek V4 Flash at $NaN/1M tokens.
For coding tasks, DeepSeek V4 Flash scores 49.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while Llama-3.3-70B scores 88.4% on human-eval (basic standalone code completion and simple functions).
Related Matchups
Explore similar comparisons for DeepSeek V4 Flash and Llama-3.3-70B.