GLM-5.2 vs Qwen 3.7

What I tested — same prompt, two models

I run the same fixed prompt set through every new model the day it drops — same string, one shot, single HTML file out — and I score the result 0–10 on whether it ran, how close it hit the brief, and how good it looked. Below is what came out when I gave the exact same prompts to GLM-5.2 and Qwen 3.7, side by side, on 47 shared tasks inside the Agent Operating System.

Both models were given identical prompts inside the Agent Operating System — no help, no iteration, no "best of N" tricks. I run each prompt once, save the HTML file the model produces, and score it 0–10 on whether it ran, how close it hit the brief, and how good it looked. The scoring is mine. The verdicts below are pulled from my source comparison guides at agentos.guide where I publish every score and the reasoning behind it.

GLM-5.2 · Default model inside Agent OS for any task that touches a long context — codebase Q&A, multi-file refactors, agent memory replay.

Qwen 3.7 · Wired alongside GLM-5.2 in Agent OS for open-weights agent loops where you want vendor diversity.

Where GLM-5.2 beat Qwen 3.7

The tasks where I gave GLM-5.2 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where Qwen 3.7 beat GLM-5.2

The tasks where I gave Qwen 3.7 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 47 scored shared tasks, GLM-5.2 averaged 7.77/10, beating Qwen 3.7's 7.00/10 by 0.77 points. Pick GLM-5.2 when the build has to ship on the first prompt and you can afford the trade-offs in the comparison below.

If you only run one of these inside your stack, the head-to-head average above is the call. If you can run both, my honest play is to wire GLM-5.2 and Qwen 3.7 both into the Agent Operating System and dispatch each from the kanban by task type — long-context agent loops — pasting a whole codebase into one prompt → GLM-5.2, open-weights alternative to glm-5.2 when you want a different model family → Qwen 3.7. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Qwen 3.7

Which is better, GLM-5.2 or Qwen 3.7?

On Goldie Bench, GLM-5.2 averages 7.77/10 across the shared tasks, with 5 gold, 0 silver, 0 bronze overall. Qwen 3.7 averages 7.00/10, with 0 gold, 0 silver, 0 bronze. GLM-5.2 wins the head-to-head 29–6.

How much does GLM-5.2 cost vs Qwen 3.7?

GLM-5.2: Open-weights release: weights downloadable from Hugging Face for self-hosting, or runnable for free on z.ai for individuals (commercial use has separate licensing). Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals.

What's the context window for GLM-5.2 vs Qwen 3.7?

GLM-5.2 has a 1,000,000 tokens context window. Qwen 3.7 has a 256,000 tokens context window.

When should I pick GLM-5.2 over Qwen 3.7?

Pick GLM-5.2 for: Long-context agent loops — pasting a whole codebase into one prompt; Cinematic visual builds — landing pages, voxel scenes, synthwave runners; Anyone who needs to run a frontier coder locally for $0. The trade-off is the weaknesses we logged on the bench: Faceplanted on the {{SITE_NAME}} raycaster — the engine was great but it spawned the player inside a wall; First-shot reliability lags Opus by a hair on consistency.

When should I pick Qwen 3.7 over GLM-5.2?

Pick Qwen 3.7 for: Open-weights alternative to GLM-5.2 when you want a different model family; Multilingual workloads (Chinese, multi-script content); Fluid and particle simulations. The trade-off is the weaknesses we logged on the bench: Only 5 tasks scored on the bench so far — small sample size; Trails GLM-5.2 on cinematic visual builds at similar pricing.

How does Goldie Bench score GLM-5.2 vs Qwen 3.7?

Every demo on this page was built by Julian Goldie inside the Agent Operating System — same fixed prompt for both models, one shot, single HTML file out. Each result gets a 0–10 score on whether it ran, how close it hit the brief, and how good it looked. The highest score on each task gets gold; second gets silver; third gets bronze. See methodology for full provenance.

Related comparisons

Other head-to-heads using the same scoring system:

Full model pages: GLM-5.2 · Qwen 3.7 · back to the leaderboard