Qwen and GLM Put Coding Agents to the Test
π‘See how Qwen and GLM trade off visual fidelity, coding strategy, speed, and token efficiency.
β‘ 30-Second TL;DR
What Changed
GLM 5.3 Flash followed the visual-similarity instruction better and produced a more detailed result.
Why It Matters
The comparison suggests that agent quality depends not only on the model but also on tool choice, prompting, and iteration behavior. It provides useful practical evidence for selecting models for code-generation tasks involving visual fidelity versus speed and token efficiency.
What To Do Next
Benchmark both models in your own opencode workflow using separate scores for visual fidelity, time-to-first-demo, token usage, and iteration persistence.
Key Points
- β’GLM 5.3 Flash followed the visual-similarity instruction better and produced a more detailed result.
- β’Qwen 3.8 Flash Next created an animated pixel-art city in about 10 minutes and 80,000 tokens.
- β’GLM used Canvas 2D and reached a playable walking simulator in about 238,000 tokens, while Qwen wrote a software renderer from scratch.
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Original source: Reddit r/LocalLLaMA β
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