Muse Spark 1.2 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 Muse Spark 1.2 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.

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

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

Where Muse Spark 1.2 beat Qwen 3.7

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.

Where Qwen 3.7 beat Muse Spark 1.2

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, Muse Spark 1.2 averaged 7.46/10, beating Qwen 3.7's 7.00/10 by 0.46 points. Pick Muse Spark 1.2 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 Muse Spark 1.2 and Qwen 3.7 both into the Agent Operating System and dispatch each from the kanban by task type — generative-art visuals → Muse Spark 1.2, 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 — Muse Spark 1.2 vs Qwen 3.7

Which is better, Muse Spark 1.2 or Qwen 3.7?

On Goldie Bench, Muse Spark 1.2 averages 7.46/10 across the shared tasks, with 0 gold, 3 silver, 4 bronze overall. Qwen 3.7 averages 7.00/10, with 0 gold, 0 silver, 0 bronze. Muse Spark 1.2 wins the head-to-head 31–16.

How much does Muse Spark 1.2 cost vs Qwen 3.7?

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. 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 Muse Spark 1.2 vs Qwen 3.7?

Muse Spark 1.2 has a 1,000,000 tokens context window. Qwen 3.7 has a 256,000 tokens context window.

When should I pick Muse Spark 1.2 over Qwen 3.7?

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.

When should I pick Qwen 3.7 over Muse Spark 1.2?

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 Muse Spark 1.2 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: Muse Spark 1.2 · Qwen 3.7 · back to the leaderboard