GPT-5.6 Sol 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 GPT-5.6 Sol 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.

GPT-5.6 Sol · Benched on GoldieBench as the flagship Sol at medium reasoning, one-shot, then headless-playtested. In the Agent OS it's the top tier of a routed stack — Sol on the hard calls, Terra for the bulk, Luna for the everyday 90%.

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

Where GPT-5.6 Sol beat Qwen 3.7

The tasks where I gave GPT-5.6 Sol a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where Qwen 3.7 beat GPT-5.6 Sol

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, GPT-5.6 Sol averaged 8.16/10, beating Qwen 3.7's 7.00/10 by 1.16 points. Pick GPT-5.6 Sol 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 GPT-5.6 Sol and Qwen 3.7 both into the Agent Operating System and dispatch each from the kanban by task type — the hardest reasoning and code where being right beats being cheap → GPT-5.6 Sol, 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 — GPT-5.6 Sol vs Qwen 3.7

Which is better, GPT-5.6 Sol or Qwen 3.7?

On Goldie Bench, GPT-5.6 Sol averages 8.16/10 across the shared tasks, with 11 gold, 11 silver, 7 bronze overall. Qwen 3.7 averages 7.00/10, with 0 gold, 0 silver, 2 bronze. GPT-5.6 Sol wins the head-to-head 43–4.

How much does GPT-5.6 Sol cost vs Qwen 3.7?

GPT-5.6 Sol: GPT-5.6 shipped as three models — Luna ($1/$6 per M), Terra ($2.50/$15) and Sol ($5/$30) — each with a same-price pro variant that ships a higher default reasoning effort. All share a ~1.05M-token context window and are rated High capability. Benched here on the flagship, Sol, at medium reasoning effort via OpenRouter. 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 GPT-5.6 Sol vs Qwen 3.7?

GPT-5.6 Sol has a 1,050,000 tokens context window. Qwen 3.7 has a 256,000 tokens context window.

When should I pick GPT-5.6 Sol over Qwen 3.7?

Pick GPT-5.6 Sol for: The hardest reasoning and code where being right beats being cheap; One-shot game/sim prototypes you want shippable on the first prompt; The flagship slot in a routed Agent OS — Sol for the hard 10%, Luna/Terra for the rest. The trade-off is the weaknesses we logged on the bench: Priciest tier on the bench at $30/M output — only worth routing the hardest 10% of work to Sol; Reasoning can eat the token budget on big open-world briefs (one 0-byte failure until the budget was raised, then it built clean).

When should I pick Qwen 3.7 over GPT-5.6 Sol?

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 GPT-5.6 Sol 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: GPT-5.6 Sol · Qwen 3.7 · back to the leaderboard