Real head-to-head · same prompt, one shot

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.

Qwen 3.7 · context256K tokens
Gemma-4 12B Coder · context256K tokens
Qwen 3.7 · priceOpen weights · free for individuals
Gemma-4 12B Coder · priceFree · runs locally
Qwen 3.7 · vendorAlibaba
Gemma-4 12B Coder · vendorCommunity (Gemma-4 · local)

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 = 🥉).

Task ↓
Qwen 3.7
Gemma-4 12B Coder
Game
🥉Qwen 3.7 on Arcade
Gemma-4 12B Coder on Arcade
Page
Qwen 3.7 on Landing
Gemma-4 12B Coder on Landing
Sim
— not attempted —
Sim
— not attempted —
Gemma-4 12B Coder on Galaxy
Sim
Qwen 3.7 on Orbit
— not attempted —
Sim
— not attempted —
Gemma-4 12B Coder on Solar
Visual
— not attempted —
Gemma-4 12B Coder on Matrix
Visual
— not attempted —
Gemma-4 12B Coder on Plasma
Visual
Qwen 3.7 on Voxel
— not attempted —

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.

Arcade Game
Qwen 3.7 8.0 · Gemma-4 12B Coder 6.0 (+2.0)

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.

Landing Page
Qwen 3.7 8.0 · Gemma-4 12B Coder 7.0 (+1.0)

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
VendorAlibabaCommunity (Gemma-4 · local)
Context window256,000 tokens256,000 tokens
PriceOpen weights · free for individualsFree · runs locally
Pricing detailAlibaba'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.
Release2026-062026-06
Bench coverage5/5 scored · avg 7.50/106/6 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.

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 3,600+ founders shipping with it every day all live inside the AI Profit Boardroom.

3,600+founders
258documented wins
38countries
$100k+/mocommunity MRR