
Real head-to-head · same prompt, one shot
Grok vs Qwen 3.7
Snappy + real-time — the X-native model. vs Multilingual open-weights — strong on Chinese reasoning.
Head-to-head verdict: Grok wins 34–1 with 8 ties.
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 Grok 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.
Grok · Used for real-time content workflows where the model needs current X timeline context. Standalone bench scoring pending.
Qwen 3.7 · Wired alongside GLM-5.2 in Agent OS for open-weights agent loops where you want vendor diversity.
Side-by-side on 47 shared tasks
Click any cell to play that model's actual one-shot attempt. Medals are derived from my 0–10 scores per task (highest = 🥇, second = 🥈, third = 🥉).
Task ↓
Grok
Qwen 3.7
Game
Game
Game
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Page
Where Grok beat Qwen 3.7
The tasks where I gave Grok a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Raycaster
Game
Grok 8.0
·
Qwen 3.7 4.0
(+4.0)
What I saw: Canvas 2D raycaster maze with WASD + mouse-look, floor/ceiling, distance fog, weapon bob. 20KB.
Reactiondiff
Sim
Grok 8.0
·
Qwen 3.7 5.0
(+3.0)
What I saw: Gray-Scott reaction-diffusion on WebGL with click-to-seed + f/k sliders. 19KB.
Rpg
Game
Grok 9.0
·
Qwen 3.7 6.0
(+3.0)
· winner · top-down RPG
What I saw: 35KB top-down RPG with tilemap, walkable terrain, NPCs, combat, HP/MP UI, inventory. Beats Fusion's lighter 26KB attempt on density.
Webos
Page
Grok 9.0
·
Qwen 3.7 6.0
(+3.0)
· winner · ambitious desktop
What I saw: Web-OS desktop with wallpaper, dock, draggable resizable windows for Notes/Paint/Terminal/Calculator. 33KB — beats Fusion's 24KB attempt on density.
Lavalamp
Visual
Grok 7.5
·
Qwen 3.7 5.0
(+2.5)
What I saw: The capsule's full of bright gooey blobs now — cyan, pink and white metaballs rising and merging. The raymarched-shader version came up empty twice, so I had it rebuilt in plain Canvas 2D, and now it renders every time.
Where Qwen 3.7 beat Grok
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.
Aurora
Visual
Qwen 3.7 7.5
·
Grok 7.0
(+0.5)
What I saw: 6KB · plays clean · webgl, rAF
Strengths & weaknesses I logged
Grok
Strengths
- Real-time access to X timeline data — unique signal no other model has
- Snappy latency on shorter prompts
- 256K context window keeps pace with the open-weights field
Trade-offs
- 13 demos on the bench but zero have curated 0–10 verdicts yet — currently unranked
- API access is gated behind X Premium, awkward for backend agent loops
Qwen 3.7
Strengths
- Open weights, free for individuals — same model class as GLM-5.2
- Best-of-three on fluid simulation in the Goldie Bench bench
- Multilingual depth — Chinese reasoning especially strong
Trade-offs
- Only 5 tasks scored on the bench so far — small sample size
- Trails GLM-5.2 on cinematic visual builds at similar pricing
Pricing & context — the spec sheet
| Spec | Grok | Qwen 3.7 |
|---|---|---|
| Vendor | xAI | Alibaba |
| Context window | 256,000 tokens | 256,000 tokens |
| Price | Subscription via X Premium | Open weights · free for individuals |
| Pricing detail | Bundled with X (Twitter) Premium subscription — no per-token bill for end users, no individual API pricing for the chat product. | Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. |
| Release | 2026-04 | 2026-06 |
| Bench coverage | 43/47 scored · avg 8.09/10 | 47/47 scored · avg 7.00/10 |
The verdict — which should you pick?
Across 43 scored shared tasks, Grok averaged 8.09/10, beating Qwen 3.7's 7.01/10 by 1.08 points. Pick Grok 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 Grok and Qwen 3.7 both into the Agent Operating System and dispatch each from the kanban by task type — workflows that need live x / twitter context → Grok, 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 — Grok vs Qwen 3.7
Which is better, Grok or Qwen 3.7?
On Goldie Bench, Grok averages 8.09/10 across the shared tasks, with 5 gold, 1 silver, 1 bronze overall. Qwen 3.7 averages 7.01/10, with 0 gold, 0 silver, 0 bronze. Grok wins the head-to-head 34–1.
How much does Grok cost vs Qwen 3.7?
Grok: Bundled with X (Twitter) Premium subscription — no per-token bill for end users, no individual API pricing for the chat product. 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 Grok vs Qwen 3.7?
Grok has a 256,000 tokens context window. Qwen 3.7 has a 256,000 tokens context window.
When should I pick Grok over Qwen 3.7?
Pick Grok for: Workflows that need live X / Twitter context; Snappy prompts where latency matters; Researchers comparing X-native models against the rest of the field. The trade-off is the weaknesses we logged on the bench: 13 demos on the bench but zero have curated 0–10 verdicts yet — currently unranked; API access is gated behind X Premium, awkward for backend agent loops.
When should I pick Qwen 3.7 over Grok?
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 Grok 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:
Grok vs Fusion Qwen 3.7 vs Fusion Grok vs Claude Opus 5 Qwen 3.7 vs Claude Opus 5 Grok vs Hermes MoA Qwen 3.7 vs Hermes MoA Grok vs GPT-5.6 Sol Qwen 3.7 vs GPT-5.6 SolFull model pages: Grok · Qwen 3.7 · back to the leaderboard
The same stack Julian uses
Run this stack yourself.
Every demo on this bench was built inside the Agent Operating System — one prompt, one shot, single HTML file out. The Agent OS, the prompts, the templates, the weekly walkthroughs and 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.
4,000+founders
258documented wins
38countries
$59/momonthly













































