GLM-5.2 vs Grok 4.7

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 Grok 4.7, side by side, on 11 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.

Grok 4.7 · Wired into the Agent OS Grok Build tab (4.7 / 4.6 / 4.5 picker, OpenRouter fallback when the CLI is signed out) and benched on twenty skill-infused game builds, each played through its full gameplay arc on a Metal GPU before the vision judge scored the played frame.

Where GLM-5.2 beat Grok 4.7

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 Grok 4.7 beat GLM-5.2

The tasks where I gave Grok 4.7 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 11 scored shared tasks, GLM-5.2 averaged 7.86/10, beating Grok 4.7's 6.86/10 by 1.00 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 Grok 4.7 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, one-shot arcade, driving and shooter builds where the whole game has to arrive in a single file → Grok 4.7. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Grok 4.7

Which is better, GLM-5.2 or Grok 4.7?

On Goldie Bench, GLM-5.2 averages 7.86/10 across the shared tasks, with 5 gold, 0 silver, 0 bronze overall. Grok 4.7 averages 6.86/10, with 0 gold, 1 silver, 2 bronze. Grok 4.7 wins the head-to-head 6–5.

How much does GLM-5.2 cost vs Grok 4.7?

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). Grok 4.7: xAI's September 2026 release, priced exactly like Grok 4.6: $2 per million input tokens and $6 per million output on the standard tier, $4 / $12 on the Fast tier (double the output speed). OpenRouter lists the same model at $1.60 / $4.80.

What's the context window for GLM-5.2 vs Grok 4.7?

GLM-5.2 has a 1,000,000 tokens context window. Grok 4.7 has a 500,000 tokens context window.

When should I pick GLM-5.2 over Grok 4.7?

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 Grok 4.7 over GLM-5.2?

Pick Grok 4.7 for: One-shot arcade, driving and shooter builds where the whole game has to arrive in a single file; High-volume agent work priced at half what the other frontier models charge; Grok Build inside the Agent OS: pick 4.7 in the tab and everything it writes lands in the workspace. The trade-off is the weaknesses we logged on the bench: 2 of 20 builds died on load or never moved when played — crypt: hud is not a function; voxelcraft: mesh.computeBoundingSphere is not a function file:///Users/j; Independent Artificial Analysis Intelligence Index v4.3.2 puts it at 46, seven points behind Claude Fable 5.1 and GPT-6 at 53; Terminal-Bench 4.0 is the weak spot xAI reports itself: 38.0% at launch.

How does Goldie Bench score GLM-5.2 vs Grok 4.7?

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 · Grok 4.7 · back to the leaderboard