GPT-Image-2.5 (Sunburst) vs Qwen 2.5 Max
A direct, empirical evaluation of GPT-Image-2.5 (Sunburst) (OpenAI) against Qwen 2.5 Max (Alibaba Cloud).
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.
Qwen 2.5 Max
Developed by Alibaba Cloud · Proprietary (~700B) parameters
Cross-lingual document extraction and structured structured output generation.
| Metric | GPT-Image-2.5 (Sunburst) | Qwen 2.5 Max |
|---|---|---|
| SWE-bench Verified | 62.4% | 51.4% |
| MMLU-Pro | 89.5% | 77.2% |
| MATH-500 | 91.2% | 89.4% |
| Context Window | Spatial Vector Token Canvas (1792×1024) | 128,000 tokens |
| Input Pricing (per 1M) | $2.50 | $1.60 |
| Output Pricing (per 1M) | $10.00 | $6.40 |
Architectural & Practical Deployment Analysis
Evaluating GPT-Image-2.5 (Sunburst) (OpenAI) alongside Qwen 2.5 Max (Alibaba Cloud) highlights key technical trade-offs across neural parameter scale and compute efficiency. GPT-Image-2.5 (Sunburst) executes across Proprietary Multimodal DiT total parameters with Continuous Flow Transformer active per token, whereas Qwen 2.5 Max operates on Proprietary (~700B) parameters with Dense/MoE Hybrid active per token.
On SWE-bench Verified software engineering tasks, GPT-Image-2.5 (Sunburst) achieves 62.4% autonomous resolution rate, compared to 51.4% for Qwen 2.5 Max. On complex reasoning benchmarks, GPT-Image-2.5 (Sunburst) posts 89.5% on MMLU-Pro and 91.2% on MATH-500, against 77.2% and 89.4% respectively for Qwen 2.5 Max.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $47.50 on GPT-Image-2.5 (Sunburst) versus $30.40 on Qwen 2.5 Max.
Recommendation: Deploy GPT-Image-2.5 (Sunburst) when your infrastructure prioritizes high-fidelity typographic rendering, raytracing reflection accuracy, sketch-to-image conditioning, and localized comment inpainting.. Deploy Qwen 2.5 Max when workload requirements favor cross-lingual document extraction and structured structured output generation..