GPT-Image-2.5 (Sunburst) vs Visual Jev (Qwen3-VL 8B Backbone)
A direct, empirical evaluation of GPT-Image-2.5 (Sunburst) (OpenAI) against Visual Jev (Qwen3-VL 8B Backbone) (Independent AI Systems Lab (Guanxu Yu & Yuhang Yao)).
GPT-Image-2.5 (Sunburst)
Developed by OpenAI · Proprietary Multimodal DiT parameters
High-fidelity typographic rendering, raytracing reflection accuracy, sketch-to-image conditioning, and localized comment inpainting.
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 | GPT-Image-2.5 (Sunburst) | Visual Jev (Qwen3-VL 8B Backbone) |
|---|---|---|
| SWE-bench Verified | 62.4% | 62.1% |
| MMLU-Pro | 89.5% | 86.4% |
| MATH-500 | 91.2% | 89.2% |
| Context Window | Spatial Vector Token Canvas (1792×1024) | 32,768 tokens (Visual prefix + batched question suffixes) |
| Input Pricing (per 1M) | $2.50 | $0.05 |
| Output Pricing (per 1M) | $10.00 | $0.10 |