GLM-5-3 (Flagship) vs GPT-Image-2.5 (Sunburst)
A direct, empirical evaluation of GLM-5-3 (Flagship) (Zhipu AI & THUDM) against GPT-Image-2.5 (Sunburst) (OpenAI).
GLM-5-3 (Flagship)
Developed by Zhipu AI & THUDM · 744B parameters
Complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.
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 | GLM-5-3 (Flagship) | GPT-Image-2.5 (Sunburst) |
|---|---|---|
| SWE-bench Verified | 54.8% | 62.4% |
| MMLU-Pro | 78.4% | 89.5% |
| MATH-500 | 93.6% | 91.2% |
| Context Window | 256,000 tokens | Spatial Vector Token Canvas (1792×1024) |
| Input Pricing (per 1M) | $0.55 | $2.50 |
| Output Pricing (per 1M) | $2.19 | $10.00 |
Architectural & Practical Deployment Analysis
Evaluating GLM-5-3 (Flagship) (Zhipu AI & THUDM) alongside GPT-Image-2.5 (Sunburst) (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. GLM-5-3 (Flagship) executes across 744B total parameters with 44B 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, GLM-5-3 (Flagship) achieves 54.8% autonomous resolution rate, compared to 62.4% for GPT-Image-2.5 (Sunburst). On complex reasoning benchmarks, GLM-5-3 (Flagship) posts 78.4% on MMLU-Pro and 93.6% 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 $10.42 on GLM-5-3 (Flagship) versus $47.50 on GPT-Image-2.5 (Sunburst).
Recommendation: Deploy GLM-5-3 (Flagship) when your infrastructure prioritizes complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.. 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..