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Alibaba Cloud (Qwen)
Qwen3.8-Flash-Next
VS
DeepSeek
DeepSeek V2 Lite
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
Qwen3.8-Flash-NextOOM / Exceeds Limit
VRAM Footprint (Q4):104.78 GB
Active GPU Load:13.04 GB
DeepSeek V2 Lite100% GPU VRAM (Safe)
VRAM Footprint (Q4):10.50 GB
Active GPU Load:10.50 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | Qwen3.8-Flash-Next | DeepSeek V2 Lite |
|---|---|---|
| Parameter Scale | 180B Parameters | 16B Parameters |
| Attention Architecture | GDN + QSA Sparse Hybrid | MLA Low-Rank Latent |
| Max Context Window | 262.14k | 163.84k |
| Available GGUF Quants | 4 Quant Formats | 0 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
Qwen3.8-Flash-Next62.5%
DeepSeek V2 Lite29.9%
Reasoning Benchmark (GPQA / MMLU)
Qwen3.8-Flash-Next91.7%
DeepSeek V2 LiteN/A%
Frequently Asked Questions about Qwen3.8-Flash-Next vs DeepSeek V2 Lite
DeepSeek V2 Lite is cheaper than Qwen3.8-Flash-Next. DeepSeek V2 Lite has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than Qwen3.8-Flash-Next at $NaN/1M tokens.
For coding tasks, Qwen3.8-Flash-Next scores 62.5% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning), while DeepSeek V2 Lite scores 29.9% on human-eval (basic standalone code completion and simple functions).
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