Claude Fable 5.1 vs GPT-Image-2.5 (Sunburst)
A direct, empirical evaluation of Claude Fable 5.1 (Anthropic) against GPT-Image-2.5 (Sunburst) (OpenAI).
Claude Fable 5.1
Developed by Anthropic · Proprietary Frontier parameters
Full-repository software refactoring, agentic tool workflows, formal verification, and 1.5M-token needle retrieval.
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 | Claude Fable 5.1 | GPT-Image-2.5 (Sunburst) |
|---|---|---|
| SWE-bench Verified | 82.8% | 62.4% |
| MMLU-Pro | 92.4% | 89.5% |
| MATH-500 | 97.9% | 91.2% |
| Context Window | 1,500,000 tokens | Spatial Vector Token Canvas (1792×1024) |
| Input Pricing (per 1M) | $12.00 | $2.50 |
| Output Pricing (per 1M) | $60.00 | $10.00 |
Architectural & Practical Deployment Analysis
Evaluating Claude Fable 5.1 (Anthropic) alongside GPT-Image-2.5 (Sunburst) (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. Claude Fable 5.1 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, Claude Fable 5.1 achieves 82.8% autonomous resolution rate, compared to 62.4% for GPT-Image-2.5 (Sunburst). On complex reasoning benchmarks, Claude Fable 5.1 posts 92.4% on MMLU-Pro and 97.9% 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 $264.00 on Claude Fable 5.1 versus $47.50 on GPT-Image-2.5 (Sunburst).
Recommendation: Deploy Claude Fable 5.1 when your infrastructure prioritizes full-repository software refactoring, agentic tool workflows, formal verification, and 1.5m-token needle retrieval.. 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..