Qwen 3.7 vs DeepSeek V4 Pro

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 Qwen 3.7 and DeepSeek V4 Pro, 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.

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

DeepSeek V4 Pro · Benched on all 50 GoldieBench tasks via api.deepseek.com with the same pipeline as the Flash 0731 run, then published as a live side-by-side: goldiebench.com/vs-live/deepseek-flash-vs-pro.html loads both builds of every task in twin panes.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Not enough scored shared tasks yet for a head-to-head average. The live demos for both are on the matrix above — play them and form your own opinion.

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 Qwen 3.7 and DeepSeek V4 Pro both into the Agent Operating System and dispatch each from the kanban by task type — open-weights alternative to glm-5.2 when you want a different model family → Qwen 3.7, checking whether deepseek's pro tier is worth the premium over flash 0731 → DeepSeek V4 Pro. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Qwen 3.7 vs DeepSeek V4 Pro

Which is better, Qwen 3.7 or DeepSeek V4 Pro?

On Goldie Bench, Qwen 3.7 averages no scored verdicts yet across the shared tasks, with 0 gold, 0 silver, 0 bronze overall. DeepSeek V4 Pro averages no scored verdicts yet, with 0 gold, 0 silver, 0 bronze. Not enough scored shared tasks yet to call a winner.

How much does Qwen 3.7 cost vs DeepSeek V4 Pro?

Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. DeepSeek V4 Pro: DeepSeek's flagship tier, benched on `deepseek-v4-pro` via api.deepseek.com — the exact same 50 one-shot prompts, skill-infused game prompt and pipeline as the V4 Flash 0731 run, so the two runs are directly comparable side by side. DeepSeek's own line on the 0731 Flash refresh is that its post-training now beats the older V4-Pro-Preview — this run tests the current Pro against that claim.

What's the context window for Qwen 3.7 vs DeepSeek V4 Pro?

Qwen 3.7 has a 256,000 tokens context window. DeepSeek V4 Pro has a 1,000,000-token context window context window.

When should I pick Qwen 3.7 over DeepSeek V4 Pro?

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.

When should I pick DeepSeek V4 Pro over Qwen 3.7?

Pick DeepSeek V4 Pro for: Checking whether DeepSeek's pro tier is worth the premium over Flash 0731; Hard single-shot builds where extra reasoning depth may pay off. The trade-off is the weaknesses we logged on the bench: Unranked — builds are on the bench but not yet scored by the Opus vision judge; Slower and pricier per build than Flash — the whole question is whether that buys quality.

How does Goldie Bench score Qwen 3.7 vs DeepSeek V4 Pro?

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: Qwen 3.7 · DeepSeek V4 Pro · back to the leaderboard