
GLM-5.2 vs North Mini Code
The never-forgets agent — 1M context, open weights. 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 GLM-5.2 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.
GLM-5.2 · Default model inside Agent OS for any task that touches a long context — codebase Q&A, multi-file refactors, agent memory replay.
North Mini Code · Wired into the Agent OS as the local-first coder for offline workflows. Bench scoring pending.
Side-by-side on 21 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
GLM-5.2
Strengths
- 1M-token context window — best-in-class long-document and large-codebase work
- Open weights — runs locally, no vendor lock-in, no token meter
- Top of the bench for cinematic visuals (neon city, synthwave, voxel runner)
Trade-offs
- Faceplanted on the Goldie Bench raycaster — the engine was great but it spawned the player inside a wall
- First-shot reliability lags Opus by a hair on consistency
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 | GLM-5.2 | North Mini Code |
|---|---|---|
| Vendor | Zhipu / Z.ai | Cohere |
| Context window | 1,000,000 tokens | Specs not yet public |
| Price | Open weights · free for individuals | Free — local |
| Pricing detail | Open-weights release: weights downloadable from Hugging Face for self-hosting, or runnable for free on z.ai for individuals (commercial use has separate licensing). | 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 | 2026-06-14 | 2026-06-19 |
| Bench coverage | 13/21 scored · avg 8.23/10 | 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 GLM-5.2 and North Mini Code both into the Agent Operating System and dispatch each from the kanban by task type — long-context agent loops — pasting a whole codebase into one prompt → GLM-5.2, 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 — GLM-5.2 vs North Mini Code
Which is better, GLM-5.2 or North Mini Code?
On Goldie Bench, GLM-5.2 averages no scored verdicts yet across the shared tasks, with 6 gold, 3 silver, 4 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 GLM-5.2 cost vs North Mini Code?
GLM-5.2: Open-weights release: weights downloadable from Hugging Face for self-hosting, or runnable for free on z.ai for individuals (commercial use has separate licensing). 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 GLM-5.2 vs North Mini Code?
GLM-5.2 has a 1,000,000 tokens context window. North Mini Code has a Specs not yet public context window.
When should I pick GLM-5.2 over North Mini Code?
Pick GLM-5.2 for: Long-context agent loops — pasting a whole codebase into one prompt; Cinematic visual builds — landing pages, voxel scenes, synthwave runners; Anyone who needs to run a frontier coder locally for $0. The trade-off is the weaknesses we logged on the bench: Faceplanted on the {{SITE_NAME}} raycaster — the engine was great but it spawned the player inside a wall; First-shot reliability lags Opus by a hair on consistency.
When should I pick North Mini Code over GLM-5.2?
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 GLM-5.2 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:
GLM-5.2 vs Opus 4.8 North Mini Code vs Opus 4.8 GLM-5.2 vs Grok North Mini Code vs Grok GLM-5.2 vs Qwen 3.7 North Mini Code vs Qwen 3.7 GLM-5.2 vs Kimi K2.7 North Mini Code vs Kimi K2.7Full model pages: GLM-5.2 · 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.
















