GPT-6 Astra vs GPT-Image-2.5 (Sunburst)
A direct, empirical evaluation of GPT-6 Astra (OpenAI) against GPT-Image-2.5 (Sunburst) (OpenAI).
GPT-6 Astra
Developed by OpenAI · Proprietary Frontier parameters
Complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and CAD synthesis.
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 | GPT-6 Astra | GPT-Image-2.5 (Sunburst) |
|---|---|---|
| SWE-bench Verified | 79.4% | 62.4% |
| MMLU-Pro | 91.2% | 89.5% |
| MATH-500 | 98.6% | 91.2% |
| Context Window | 1,050,000 tokens | Spatial Vector Token Canvas (1792×1024) |
| Input Pricing (per 1M) | $10.00 | $2.50 |
| Output Pricing (per 1M) | $50.00 | $10.00 |
Architectural & Practical Deployment Analysis
Evaluating GPT-6 Astra (OpenAI) alongside GPT-Image-2.5 (Sunburst) (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. GPT-6 Astra executes across Proprietary Frontier total parameters with Dynamic Recurrent MoE 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, GPT-6 Astra achieves 79.4% autonomous resolution rate, compared to 62.4% for GPT-Image-2.5 (Sunburst). On complex reasoning benchmarks, GPT-6 Astra posts 91.2% on MMLU-Pro and 98.6% 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 $220.00 on GPT-6 Astra versus $47.50 on GPT-Image-2.5 (Sunburst).
Recommendation: Deploy GPT-6 Astra when your infrastructure prioritizes complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and cad synthesis.. 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..