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

Gemma-4 12B Coder vs Kimi K2.7 · No-Think

The free, offline coder — trained only on code that passed its tests. vs Pure execution mode — no chain of thought.

Gemma-4 12B Coder · context256K tokens
Kimi K2.7 · No-Think · context256K tokens
Gemma-4 12B Coder · priceFree · runs locally
Kimi K2.7 · No-Think · priceFlat plan (no per-token bill)
Gemma-4 12B Coder · vendorCommunity (Gemma-4 · local)
Kimi K2.7 · No-Think · vendorMoonshot AI

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 Gemma-4 12B Coder and Kimi K2.7 · No-Think, 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.

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.

Kimi K2.7 · No-Think · Reserved for templated transforms where the plan is already in the prompt — the model just executes.

Side-by-side on 7 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 ↓
Gemma-4 12B Coder
Kimi K2.7 · No-Think
Sim
Gemma-4 12B Coder on Galaxy
Kimi K2.7 · No-Think on Galaxy
Sim
Gemma-4 12B Coder on Solar
Kimi K2.7 · No-Think on Solar
Game
Gemma-4 12B Coder on Arcade
— not attempted —
Game
— not attempted —
Kimi K2.7 · No-Think on Game
Page
Gemma-4 12B Coder on Landing
— not attempted —
Visual
Gemma-4 12B Coder on Matrix
— not attempted —
Visual
Gemma-4 12B Coder on Plasma
— not attempted —

Strengths & weaknesses I logged

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

Kimi K2.7 · No-Think

Strengths

  • Skips planning to ship straight to code
  • Useful when you've already done the reasoning in the prompt
  • Predictable latency for batched jobs

Trade-offs

  • Loses ground on multi-step tasks that benefit from planning
  • Not scored on the standalone bench — see methodology

Pricing & context — the spec sheet

Spec Gemma-4 12B Coder Kimi K2.7 · No-Think
VendorCommunity (Gemma-4 · local)Moonshot AI
Context window256,000 tokens256,000 tokens
PriceFree · runs locallyFlat plan (no per-token bill)
Pricing detailA 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.Same flat-rate plan as standard Kimi K2.7 — No-Think disables the chain-of-thought layer at runtime.
Release2026-062026-06
Bench coverage6/6 scored · avg 4.25/100/3 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 Gemma-4 12B Coder and Kimi K2.7 · No-Think both into the Agent Operating System and dispatch each from the kanban by task type — free, private, offline coding where nothing can leave your machine → Gemma-4 12B Coder, templated transforms where the plan is in the prompt → Kimi K2.7 · No-Think. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — Gemma-4 12B Coder vs Kimi K2.7 · No-Think

Which is better, Gemma-4 12B Coder or Kimi K2.7 · No-Think?

On Goldie Bench, Gemma-4 12B Coder averages no scored verdicts yet across the shared tasks, with 0 gold, 0 silver, 0 bronze overall. Kimi K2.7 · No-Think 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 Gemma-4 12B Coder cost vs Kimi K2.7 · No-Think?

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. Kimi K2.7 · No-Think: Same flat-rate plan as standard Kimi K2.7 — No-Think disables the chain-of-thought layer at runtime.

What's the context window for Gemma-4 12B Coder vs Kimi K2.7 · No-Think?

Gemma-4 12B Coder has a 256,000 tokens context window. Kimi K2.7 · No-Think has a 256,000 tokens context window.

When should I pick Gemma-4 12B Coder over Kimi K2.7 · No-Think?

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.

When should I pick Kimi K2.7 · No-Think over Gemma-4 12B Coder?

Pick Kimi K2.7 · No-Think for: Templated transforms where the plan is in the prompt; Batched code generation jobs; Workflows where you want the model to stop second-guessing. The trade-off is the weaknesses we logged on the bench: Loses ground on multi-step tasks that benefit from planning; Not scored on the standalone bench — see methodology.

How does Goldie Bench score Gemma-4 12B Coder vs Kimi K2.7 · No-Think?

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