GLM 5.2 vs. Opus 4.8 vs. GPT 5.5 Performance Comparison
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the gist
GLM 5.2 underperforms against frontier models like Opus 4.8 and GPT 5.5 in agentic coding tasks and creative UI generation, while consuming significantly more tokens to achieve inferior results.
Agentic Coding Performance
In the DeepSWE benchmark, which evaluates long-running agentic tasks across TypeScript, Go, Python, JavaScript, and Rust, GLM 5.2 Max achieves a 44% success rate at a cost of $3.92 per task. In comparison, Opus 4.8 reaches 59% and GPT 5.5 hits 67% at their respective high-effort settings. While GLM 5.2 is cheaper on a per-million-token basis ($1.40 input / $4.40 output), it is less efficient in practice because it requires significantly higher token volumes to complete the same tasks as the frontier models.
Real-World Task Execution
When tasked with building a 3D browser-based racing game and an award-style landing page, GLM 5.2 consistently struggled with output quality and token efficiency.
- Game Development: GLM 5.2 produced janky physics and inconsistent track geometry, requiring over 1 million tokens compared to roughly 100,000 tokens for Opus 4.8 and GPT 5.5.
- UI Design: In landing page generation, GLM 5.2 failed to render a functional layout on the first attempt, whereas GPT 5.5 provided the most coherent visual hierarchy and 3D integration using Three.js.
- Resource Usage: GLM 5.2 is not a local-runnable model; it requires substantial hardware infrastructure, contradicting the common perception that its open-source nature makes it a lightweight or easily deployable alternative to proprietary APIs.
Conclusion
For individual users, the subsidized pricing plans for Claude and OpenAI models make them more cost-effective and performant than GLM 5.2. The model shows promise as an open-source offering, but it currently lags behind the frontier giants in both reasoning capability and token-to-outcome efficiency.