DeepSeek V4.1 Flash vs GPT-Image-2.5 (Sunburst)
A direct, empirical evaluation of DeepSeek V4.1 Flash (DeepSeek-AI) against GPT-Image-2.5 (Sunburst) (OpenAI).
DeepSeek V4.1 Flash
Developed by DeepSeek-AI · 552B parameters
High-throughput software engineering, autonomous coding loops, terminal agents, 1M long-context retrieval, and low-cost production inference.
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.
| Metric | DeepSeek V4.1 Flash | GPT-Image-2.5 (Sunburst) |
|---|---|---|
| SWE-bench Verified | 67.2% | 62.4% |
| MMLU-Pro | 78.4% | 89.5% |
| MATH-500 | 92.6% | 91.2% |
| Context Window | 1,000,000 tokens | Spatial Vector Token Canvas (1792×1024) |
| Input Pricing (per 1M) | $0.06 | $2.50 |
| Output Pricing (per 1M) | $0.22 | $10.00 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek V4.1 Flash (DeepSeek-AI) alongside GPT-Image-2.5 (Sunburst) (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek V4.1 Flash executes across 552B total parameters with 8B input / 16B output active per token, whereas GPT-Image-2.5 (Sunburst) operates on Proprietary Multimodal DiT parameters with Continuous Flow Transformer active per token.
On SWE-bench Verified software engineering tasks, DeepSeek V4.1 Flash achieves 67.2% autonomous resolution rate, compared to 62.4% for GPT-Image-2.5 (Sunburst). On complex reasoning benchmarks, DeepSeek V4.1 Flash posts 78.4% on MMLU-Pro and 92.6% on MATH-500, against 89.5% and 91.2% respectively for GPT-Image-2.5 (Sunburst).
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $1.04 on DeepSeek V4.1 Flash versus $47.50 on GPT-Image-2.5 (Sunburst).
Recommendation: Deploy DeepSeek V4.1 Flash when your infrastructure prioritizes high-throughput software engineering, autonomous coding loops, terminal agents, 1m long-context retrieval, and low-cost production inference.. Deploy GPT-Image-2.5 (Sunburst) when workload requirements favor high-fidelity typographic rendering, raytracing reflection accuracy, sketch-to-image conditioning, and localized comment inpainting..