GLM-5.2 vs Muse Spark 1.2

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 Muse Spark 1.2, 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.

Muse Spark 1.2 · Cloud coder via OpenRouter; the Muse Code agent (one-command install) is its native harness.

Where GLM-5.2 beat Muse Spark 1.2

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 Muse Spark 1.2 beat GLM-5.2

The tasks where I gave Muse Spark 1.2 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, the averages are essentially tied — GLM-5.2 7.77 vs Muse Spark 1.2 7.50. This isn't the comparison where one wins; it's the comparison where you pick based on context, pricing, and what you're actually trying to ship.

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 Muse Spark 1.2 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, generative-art visuals → Muse Spark 1.2. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Muse Spark 1.2

Which is better, GLM-5.2 or Muse Spark 1.2?

On Goldie Bench, GLM-5.2 averages 7.77/10 across the shared tasks, with 5 gold, 0 silver, 0 bronze overall. Muse Spark 1.2 averages 7.50/10, with 0 gold, 3 silver, 4 bronze. Muse Spark 1.2 wins the head-to-head 26–21.

How much does GLM-5.2 cost vs Muse Spark 1.2?

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). Muse Spark 1.2: Meta's coding-optimized reasoning model, released 2026-08-05 beside the Muse Code agent. $0.15/1M cached input. Contributor tier is token-rate-limited in a rolling 5-hour window. Benched release-day via OpenRouter (meta/muse-spark-1.2, first-party listing); Opus 4.8 judged every real rendered poster, same rubric as the whole field.

What's the context window for GLM-5.2 vs Muse Spark 1.2?

GLM-5.2 has a 1,000,000 tokens context window. Muse Spark 1.2 has a 1,000,000 tokens context window.

When should I pick GLM-5.2 over Muse Spark 1.2?

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 Muse Spark 1.2 over GLM-5.2?

Pick Muse Spark 1.2 for: Generative-art visuals; Dashboard & app-shell one-shots; Long-context refactors (1M window). The trade-off is the weaknesses we logged on the bench: 3D game worlds often render black/empty (dragonrealm 2.5, dogfight 3.0, doom 3.5); Open-world briefs collapse to HUD-only shells; Reasoning tokens billed as output.

How does Goldie Bench score GLM-5.2 vs Muse Spark 1.2?

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 · Muse Spark 1.2 · back to the leaderboard