I Tested GLM 5.2 vs Opus 4.8 vs GPT 5.5

Highlights

• GLM 5.2 scores 44% accuracy on DeepSuite agentic tasks compared to 59% for Opus 4.8 and 67% for GPT 5.5.

• Cost per task on DeepSuite is $3.92 for GLM 5.2, $13.00 for Opus 4.8 at max settings, and $7.23 for GPT 5.5 at extra high settings.

• GLM 5.2 consumes over 1 million tokens for tasks where Opus 4.8 and GPT 5.5 require approximately 100,000 tokens.

• Despite lower API prices per million tokens, GLM 5.2 is often less efficient in total cost per task due to higher token consumption.

• GLM 5.2 requires significant enterprise-grade hardware to run, contradicting the perception that it is a lightweight, local-runnable model.

Timeline

1. 00:00 - 05:20

   Benchmarking GLM 5.2 vs Frontier Models

   The DeepSuite benchmark evaluates models across 113 tasks in languages including TypeScript, Go, Python, JavaScript, and Rust. Although GLM 5.2 is marketed as a cost-effective alternative, the data shows that its inefficiency in token usage offsets its lower base price per million tokens. Furthermore, users often benefit from heavily subsidized plans for Opus and GPT, making the cost argument against these frontier models less compelling.

   • DeepSuite results show GLM 5.2 achieving 44% accuracy on long-horizon agentic tasks.
   • Opus 4.8 and GPT 5.5 achieve higher accuracy scores of 59% and 67% respectively on the same benchmark.
   • Per-token pricing is significantly lower for GLM 5.2, but total cost per task is often higher because the model uses more tokens to complete assignments.
2. 05:21 - 14:03

   Agentic Performance in 3D Game Development

   Models were tested on their ability to build a 3D racing game in a browser environment using a vague prompt. Opus 4.8 finished first and provided the most playable results. Subsequent passes to improve graphics demonstrated that while all models improved slightly, GLM 5.2's output remained the least stable and most resource-intensive.

   • Opus 4.8 consistently delivers smoother, more functional 3D game environments compared to GLM 5.2 and GPT 5.5.
   • GLM 5.2 and GPT 5.5 both struggle with physics, graphical fidelity, and inconsistent control schemes during iterative testing.
   • Token usage for GLM 5.2 exceeds 1 million tokens for a single game build, while competitive models achieve better results with approximately 100,000 tokens.
3. 14:04 - 22:15

   Website Landing Page Design Capabilities

   The models were tasked with building a landing page for smart glasses that incorporated motion and 3D elements via Three.js. GLM 5.2 performed poorly, initially failing to generate a usable site, while GPT 5.5 eventually produced the best-received result after being pushed to include 3D assets. Ultimately, the testing confirms that GLM 5.2 remains a tier below the leading frontier models in both design execution and efficiency.

   • GPT 5.5 provides the most visually cohesive landing page design compared to GLM 5.2 and Opus 4.8.
   • GLM 5.2 fails to produce a functional or well-laid-out website in the initial test, requiring a second pass to achieve basic utility.
   • None of the models fully capture high-end 'award-style' design standards, as they all struggle with layout, typography, and visual hierarchy.