
Qwen 3.7 vs North Mini Code
Multilingual open-weights — strong on Chinese reasoning. vs Cohere's free coder that beats models 4× its size, runs on your own Mac.
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 North Mini Code, side by side, on 0 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.
North Mini Code · Wired into the Agent OS as the local-first coder for offline workflows. Bench scoring pending.
Side-by-side on 5 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 = 🥉).
Strengths & weaknesses I logged
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
North Mini Code
Strengths
- Tiny + fast — runs locally with no GPU farm required
- Out-scores models 4× its parameter count on agent-coding benchmarks (Cohere's own number)
- Zero cost — free for individuals, runs offline
Trade-offs
- Cohere's own number, not independently verified
- No goldiebench per-task scores yet
Pricing & context — the spec sheet
| Spec | Qwen 3.7 | North Mini Code |
|---|---|---|
| Vendor | Alibaba | Cohere |
| Context window | 256,000 tokens | Specs not yet public |
| Price | Open weights · free for individuals | Free — local |
| Pricing detail | Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. | Cohere's free coding model — small parameter count, runs locally, designed to out-punch models four times its size on agent-coding benchmarks. No token bill, no API key required. |
| Release | 2026-06 | 2026-06-19 |
| Bench coverage | 5/5 scored · avg 7.50/10 | 0/0 scored · avg — |
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 North Mini Code 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, local coding loops on a mac where you don't want a token meter → North Mini Code. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Qwen 3.7 vs North Mini Code
Which is better, Qwen 3.7 or North Mini Code?
On Goldie Bench, Qwen 3.7 averages no scored verdicts yet across the shared tasks, with 0 gold, 1 silver, 2 bronze overall. North Mini Code 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 North Mini Code?
Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. North Mini Code: Cohere's free coding model — small parameter count, runs locally, designed to out-punch models four times its size on agent-coding benchmarks. No token bill, no API key required.
What's the context window for Qwen 3.7 vs North Mini Code?
Qwen 3.7 has a 256,000 tokens context window. North Mini Code has a Specs not yet public context window.
When should I pick Qwen 3.7 over North Mini Code?
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 North Mini Code over Qwen 3.7?
Pick North Mini Code for: Local coding loops on a Mac where you don't want a token meter; Offline / air-gapped agent workflows; Operators who want to compare a small-but-mighty model against the larger field. The trade-off is the weaknesses we logged on the bench: Cohere's own number, not independently verified; No goldiebench per-task scores yet.
How does Goldie Bench score Qwen 3.7 vs North Mini Code?
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:
Qwen 3.7 vs Opus 4.8 North Mini Code vs Opus 4.8 Qwen 3.7 vs GLM-5.2 North Mini Code vs GLM-5.2 Qwen 3.7 vs Grok North Mini Code vs Grok Qwen 3.7 vs Kimi K2.7 North Mini Code vs Kimi K2.7Full model pages: Qwen 3.7 · North Mini Code · back to the leaderboard
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 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.


