Qwen 3.7 vs Inkling

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 Inkling, 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.

Inkling · Benched on GoldieBench one-shot through Tinker's OpenAI-compatible endpoint at medium reasoning effort, then headless-playtested on the same rubric as the whole field. In the Agent OS it's wired into the opencode tab on your own Tinker key — the Ink Machine.

Where Qwen 3.7 beat Inkling

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.

Where Inkling beat Qwen 3.7

The tasks where I gave Inkling 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, Qwen 3.7 averaged 7.00/10, beating Inkling's 6.00/10 by 1.00 points. Pick Qwen 3.7 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 Qwen 3.7 and Inkling 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, owning a frontier model instead of renting one — on your own key, pennies per build → Inkling. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Qwen 3.7 vs Inkling

Which is better, Qwen 3.7 or Inkling?

On Goldie Bench, Qwen 3.7 averages 7.00/10 across the shared tasks, with 0 gold, 0 silver, 2 bronze overall. Inkling averages 6.00/10, with 0 gold, 3 silver, 1 bronze. Qwen 3.7 wins the head-to-head 34–13.

How much does Qwen 3.7 cost vs Inkling?

Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. Inkling: Inkling is open-weights — a 975B-parameter (41B active) Mixture-of-Experts model whose full weights are public on Hugging Face. You run it on your own key through Tinker's OpenAI-compatible endpoint (usage-based, ~$0.33/M sampling, 50% off at launch), or via Together / Fireworks / Modal / Databricks / Baseten. Benched here one-shot at medium reasoning effort via Tinker.

What's the context window for Qwen 3.7 vs Inkling?

Qwen 3.7 has a 256,000 tokens context window. Inkling has a 1,000,000 tokens context window.

When should I pick Qwen 3.7 over Inkling?

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 Inkling over Qwen 3.7?

Pick Inkling for: Owning a frontier model instead of renting one — on your own key, pennies per build; Generative visuals, data-viz and single-file web builds you want one-shot; A customizable open base you can fine-tune on Tinker for your own domain. The trade-off is the weaknesses we logged on the bench: One-shot 3D games are weak — three.js dungeons/racers render a title screen but no playable scene, like most open models (crypt 2.5); Physics and particle sims are hit-or-miss — black-hole, plasma and cloth one-shots often render dark or static (2.3–3.5); Not the strongest overall — the closed frontier (Fable 5) still tops the raw benchmarks; Inkling trades peak for ownership.

How does Goldie Bench score Qwen 3.7 vs Inkling?

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 · Inkling · back to the leaderboard