DeepSeek-V4.1-Flash-1017 vs Visual Jev (Qwen3-VL 8B Backbone)
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against Visual Jev (Qwen3-VL 8B Backbone) (Independent AI Systems Lab (Guanxu Yu & Yuhang Yao)).
DeepSeek-V4.1-Flash-1017
Developed by DeepSeek · Open-Weight Distilled Transformer (MIT License) parameters
Top-tier open-weight coding (81.6% SWE-bench Verified), concise reasoning traces with 32% lower token bloat, and fast consumer GPU self-hosting.
Visual Jev (Qwen3-VL 8B Backbone)
Developed by Independent AI Systems Lab (Guanxu Yu & Yuhang Yao) · 8.2 Billion Vision-Language Parameters parameters
Sub-20ms multi-query image verification, automated rejection sampling in generative diffusion pipelines (FLUX / SD3), robotics perception routing, and high-frequency visual software inspection.
| Metric | DeepSeek-V4.1-Flash-1017 | Visual Jev (Qwen3-VL 8B Backbone) |
|---|---|---|
| SWE-bench Verified | 81.6% | 62.1% |
| MMLU-Pro | 92.8% | 86.4% |
| MATH-500 | 94.8% | 89.2% |
| Context Window | 128,000 tokens | 32,768 tokens (Visual prefix + batched question suffixes) |
| Input Pricing (per 1M) | $0.14 | $0.05 |
| Output Pricing (per 1M) | $0.28 | $0.10 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside Visual Jev (Qwen3-VL 8B Backbone) (Independent AI Systems Lab (Guanxu Yu & Yuhang Yao)) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek-V4.1-Flash-1017 executes across Open-Weight Distilled Transformer (MIT License) total parameters with Multi-Head Latent Attention (MLA) Core active per token, whereas Visual Jev (Qwen3-VL 8B Backbone) operates on 8.2 Billion Vision-Language Parameters parameters with 8.2B (Pinned Shared Prefix KV Cache) active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 62.1% for Visual Jev (Qwen3-VL 8B Backbone). On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 86.4% and 89.2% respectively for Visual Jev (Qwen3-VL 8B Backbone).
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $1.82 on DeepSeek-V4.1-Flash-1017 versus $0.65 on Visual Jev (Qwen3-VL 8B Backbone).
Recommendation: Deploy DeepSeek-V4.1-Flash-1017 when your infrastructure prioritizes top-tier open-weight coding (81.6% swe-bench verified), concise reasoning traces with 32% lower token bloat, and fast consumer gpu self-hosting.. Deploy Visual Jev (Qwen3-VL 8B Backbone) when workload requirements favor sub-20ms multi-query image verification, automated rejection sampling in generative diffusion pipelines (flux / sd3), robotics perception routing, and high-frequency visual software inspection..