GLM-5-3 (Flagship) vs GPT-6 Astra
A direct, empirical evaluation of GLM-5-3 (Flagship) (Zhipu AI & THUDM) against GPT-6 Astra (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-6 Astra
Developed by OpenAI · Proprietary Frontier parameters
Complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and CAD synthesis.
| Metric | GLM-5-3 (Flagship) | GPT-6 Astra |
|---|---|---|
| SWE-bench Verified | 54.8% | 79.4% |
| MMLU-Pro | 78.4% | 91.2% |
| MATH-500 | 93.6% | 98.6% |
| Context Window | 256,000 tokens | 1,050,000 tokens |
| Input Pricing (per 1M) | $0.55 | $10.00 |
| Output Pricing (per 1M) | $2.19 | $50.00 |
Architectural & Practical Deployment Analysis
Evaluating GLM-5-3 (Flagship) (Zhipu AI & THUDM) alongside GPT-6 Astra (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-6 Astra operates on Proprietary Frontier parameters with Dynamic Recurrent MoE active per token.
On SWE-bench Verified software engineering tasks, GLM-5-3 (Flagship) achieves 54.8% autonomous resolution rate, compared to 79.4% for GPT-6 Astra. On complex reasoning benchmarks, GLM-5-3 (Flagship) posts 78.4% on MMLU-Pro and 93.6% on MATH-500, against 91.2% and 98.6% respectively for GPT-6 Astra.
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 $220.00 on GPT-6 Astra.
Recommendation: Deploy GLM-5-3 (Flagship) when your infrastructure prioritizes complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.. Deploy GPT-6 Astra when workload requirements favor complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and cad synthesis..