
Real head-to-head · same prompt, one shot
Gemma-4 12B Coder vs Kilo Code
The free, offline coder — trained only on code that passed its tests. vs Fable 5-class intelligence at ~59% less. The split-the-cost play.
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 Kilo 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.
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.
Kilo Code · Used inside Agent OS as a routing layer: Fable 5 generates the plan, cheaper models execute. Bench scoring pending a head-to-head comparison.
Side-by-side on 6 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
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
Kilo Code
Strengths
- Kilo's own rubric: Fable 5 plan = 9.1/10, GPT-5.5 plan = 8.3/10 — Kilo isolates where the intelligence actually lives
- Plan quality stays high while execution cost drops
- Drop-in for Agent OS — Kilo Split framework already wired
Trade-offs
- Adds routing complexity — two model providers in one workflow
- No per-task goldiebench head-to-heads yet
Pricing & context — the spec sheet
| Spec | Gemma-4 12B Coder | Kilo Code |
|---|---|---|
| Vendor | Community (Gemma-4 · local) | Kilo |
| Context window | 256,000 tokens | Varies — Kilo splits planning from execution across multiple models |
| Price | Free · runs locally | ~59% less than Fable 5 solo |
| Pricing detail | 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. | Kilo Code is a routing layer that splits planning (heavy model) from execution (cheaper model) so you get Fable-5-class plans driving GPT-5.5-class builds. Total spend lands at ~59% less than running Fable 5 end-to-end. |
| Release | 2026-06 | 2026-06-16 |
| Bench coverage | 6/6 scored · avg 4.25/10 | 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 Gemma-4 12B Coder and Kilo Code 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, cost-conscious operators who run high-volume agent loops → Kilo Code. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Gemma-4 12B Coder vs Kilo Code
Which is better, Gemma-4 12B Coder or Kilo Code?
On Goldie Bench, Gemma-4 12B Coder averages no scored verdicts yet across the shared tasks, with 0 gold, 0 silver, 0 bronze overall. Kilo 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 Gemma-4 12B Coder cost vs Kilo Code?
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. Kilo Code: Kilo Code is a routing layer that splits planning (heavy model) from execution (cheaper model) so you get Fable-5-class plans driving GPT-5.5-class builds. Total spend lands at ~59% less than running Fable 5 end-to-end.
What's the context window for Gemma-4 12B Coder vs Kilo Code?
Gemma-4 12B Coder has a 256,000 tokens context window. Kilo Code has a Varies — Kilo splits planning from execution across multiple models context window.
When should I pick Gemma-4 12B Coder over Kilo Code?
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 Kilo Code over Gemma-4 12B Coder?
Pick Kilo Code for: Cost-conscious operators who run high-volume agent loops; Multi-step workflows where the plan is the expensive part; Teams already paying for Fable 5 who want to keep the plan but drop the execution bill. The trade-off is the weaknesses we logged on the bench: Adds routing complexity — two model providers in one workflow; No per-task goldiebench head-to-heads yet.
How does Goldie Bench score Gemma-4 12B Coder vs Kilo 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:
Gemma-4 12B Coder vs Opus 4.8 Kilo Code vs Opus 4.8 Gemma-4 12B Coder vs GLM-5.2 Kilo Code vs GLM-5.2 Gemma-4 12B Coder vs Grok Kilo Code vs Grok Gemma-4 12B Coder vs Fusion Kilo Code vs FusionFull model pages: Gemma-4 12B Coder · Kilo Code · back to the leaderboard
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




