GLM-5-3 (Flagship) vs Muse Spark 1.3 (xhigh)
A direct, empirical evaluation of GLM-5-3 (Flagship) (Zhipu AI & THUDM) against Muse Spark 1.3 (xhigh) (Meta AI).
GLM-5-3 (Flagship)
Developed by Zhipu AI & THUDM · 744B parameters
Complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.
Muse Spark 1.3 (xhigh)
Developed by Meta AI · Proprietary Multimodal parameters
Autonomous codebase refactoring, terminal execution, million-token long context retrieval, and agentic workflows.
| Metric | GLM-5-3 (Flagship) | Muse Spark 1.3 (xhigh) |
|---|---|---|
| SWE-bench Verified | 54.8% | 75.4% |
| MMLU-Pro | 78.4% | 88.4% |
| MATH-500 | 93.6% | 94.8% |
| Context Window | 256,000 tokens | 1,000,000 tokens |
| Input Pricing (per 1M) | $0.55 | $1.25 |
| Output Pricing (per 1M) | $2.19 | $5.00 |
Architectural & Practical Deployment Analysis
Evaluating GLM-5-3 (Flagship) (Zhipu AI & THUDM) alongside Muse Spark 1.3 (xhigh) (Meta AI) 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 Muse Spark 1.3 (xhigh) operates on Proprietary Multimodal parameters with Dense Reasoner active per token.
On SWE-bench Verified software engineering tasks, GLM-5-3 (Flagship) achieves 54.8% autonomous resolution rate, compared to 75.4% for Muse Spark 1.3 (xhigh). On complex reasoning benchmarks, GLM-5-3 (Flagship) posts 78.4% on MMLU-Pro and 93.6% on MATH-500, against 88.4% and 94.8% respectively for Muse Spark 1.3 (xhigh).
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 $23.75 on Muse Spark 1.3 (xhigh).
Recommendation: Deploy GLM-5-3 (Flagship) when your infrastructure prioritizes complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.. Deploy Muse Spark 1.3 (xhigh) when workload requirements favor autonomous codebase refactoring, terminal execution, million-token long context retrieval, and agentic workflows..