
Qwen 3.7 vs Gemma-4 12B Coder
Multilingual open-weights — strong on Chinese reasoning. vs The free, offline coder — trained only on code that passed its tests.
Head-to-head verdict: Qwen 3.7 wins 2–0.
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 Gemma-4 12B Coder, side by side, on 2 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.
Gemma-4 12B Coder · Wired into the Agent OS local engine (Local chat + Local Hermes Engine + Agent Kanban) as the free, offline coder. Scored by Claude judge against the same one-shot prompts every other model ran.
Side-by-side on 9 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 = 🥉).
Where Qwen 3.7 beat Gemma-4 12B Coder
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.
What I saw: The closest test. All three shipped a real, juicy game. Opus's breakout had the most game-feel (particle bursts + live combo). Qwen's neon breakout is clean and vibrant. GLM went its own way with fullscreen asteroids. Genuinely hard to separate.
What I saw: GLM and Opus both produced premium gradient 'Intelligence, reimagined / distilled' keynote heroes — basically a tie. Qwen's is clean and well-built (proper nav + three feature cards) but the headline ('Built for the next generation of builders') lands flatter than the gradient heroes.
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
Gemma-4 12B Coder
Strengths
- Runs 100% free + offline on a consumer Mac (Q4_K_M, 7.4GB) — no API, no rate limits, nothing leaves the machine
- Test-verified training (Composer 2.5 + Fable 5) — shipped a clean SaaS landing page and a working particle galaxy one-shot
- Fast on Apple Silicon — 2.4s cold start, ~35 tokens/sec on an M4 Max
Trade-offs
- Half its one-shots shipped broken on the bench — a missing canvas append, a missing render loop, and an uncompiled WebGL shader
- Far below frontier models on complex 3D / WebGL / games — strongest on pages and simple canvas work, not simulations
Pricing & context — the spec sheet
| Spec | Qwen 3.7 | Gemma-4 12B Coder |
|---|---|---|
| Vendor | Alibaba | Community (Gemma-4 · local) |
| Context window | 256,000 tokens | 256,000 tokens |
| Price | Open weights · free for individuals | Free · runs locally |
| Pricing detail | Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. | A community fine-tune of Google's Gemma-4 12B (xentriom/gemma-4-12B-coder-fable5-composer2.5-v1), Apache-2.0. Free to download and run 100% offline on your own Mac via Ollama — no API, no per-token bill. The Q4_K_M build is 7.4GB. |
| Release | 2026-06 | 2026-06 |
| Bench coverage | 5/5 scored · avg 7.50/10 | 6/6 scored · avg 4.25/10 |
The verdict — which should you pick?
Across 2 scored shared tasks, Qwen 3.7 averaged 8.00/10, beating Gemma-4 12B Coder's 6.50/10 by 1.50 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 Gemma-4 12B Coder 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, free, private, offline coding where nothing can leave your machine → Gemma-4 12B Coder. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Qwen 3.7 vs Gemma-4 12B Coder
Which is better, Qwen 3.7 or Gemma-4 12B Coder?
On Goldie Bench, Qwen 3.7 averages 8.00/10 across the shared tasks, with 0 gold, 0 silver, 1 bronze overall. Gemma-4 12B Coder averages 6.50/10, with 0 gold, 0 silver, 0 bronze. Qwen 3.7 wins the head-to-head 2–0.
How much does Qwen 3.7 cost vs Gemma-4 12B Coder?
Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals. Gemma-4 12B Coder: A community fine-tune of Google's Gemma-4 12B (xentriom/gemma-4-12B-coder-fable5-composer2.5-v1), Apache-2.0. Free to download and run 100% offline on your own Mac via Ollama — no API, no per-token bill. The Q4_K_M build is 7.4GB.
What's the context window for Qwen 3.7 vs Gemma-4 12B Coder?
Qwen 3.7 has a 256,000 tokens context window. Gemma-4 12B Coder has a 256,000 tokens context window.
When should I pick Qwen 3.7 over Gemma-4 12B Coder?
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 Gemma-4 12B Coder over Qwen 3.7?
Pick Gemma-4 12B Coder for: Free, private, offline coding where nothing can leave your machine; Landing pages, simple canvas builds, and code you'll review before shipping; Anyone who wants a $0 local coder wired into their Agent OS. The trade-off is the weaknesses we logged on the bench: Half its one-shots shipped broken on the bench — a missing canvas append, a missing render loop, and an uncompiled WebGL shader; Far below frontier models on complex 3D / WebGL / games — strongest on pages and simple canvas work, not simulations.
How does Goldie Bench score Qwen 3.7 vs Gemma-4 12B Coder?
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 Gemma-4 12B Coder vs Opus 4.8 Qwen 3.7 vs GLM-5.2 Gemma-4 12B Coder vs GLM-5.2 Qwen 3.7 vs Grok Gemma-4 12B Coder vs Grok Qwen 3.7 vs Fusion Gemma-4 12B Coder vs FusionFull model pages: Qwen 3.7 · Gemma-4 12B Coder · 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.








