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

GLM-5.2 vs Kimi K2.7 · Fast

The never-forgets agent — 1M context, open weights. vs Fast mode — top speed, minimal thinking.

GLM-5.2 · context1M tokens
Kimi K2.7 · Fast · context256K tokens
GLM-5.2 · priceOpen weights · free for individuals
Kimi K2.7 · Fast · priceFlat plan (no per-token bill)
GLM-5.2 · vendorZhipu / Z.ai
Kimi K2.7 · Fast · 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 GLM-5.2 and Kimi K2.7 · Fast, 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.

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.

Kimi K2.7 · Fast · Wired into Agent OS as the snappy default — first-pass attempts, agent chatter, live demos.

Side-by-side on 22 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 ↓
GLM-5.2
Kimi K2.7 · Fast
Sim
🥈GLM-5.2 on Galaxy
Kimi K2.7 · Fast on Galaxy
Sim
🥇GLM-5.2 on Solar
Kimi K2.7 · Fast on Solar
Game
🥈GLM-5.2 on Arcade
— not attempted —
Game
GLM-5.2 on Dogfight
— not attempted —
Game
🥉GLM-5.2 on Doom
— not attempted —
Game
— not attempted —
Kimi K2.7 · Fast on Game
Game
🥇GLM-5.2 on Neoncity
— not attempted —
Game
🥇GLM-5.2 on Outrun
— not attempted —
Game
GLM-5.2 on Pool
— not attempted —
Game
GLM-5.2 on Racing
— not attempted —
Game
🥉GLM-5.2 on Raycaster
— not attempted —
Game
GLM-5.2 on Rpg
— not attempted —
Page
🥇GLM-5.2 on Landing
— not attempted —
Sim
🥈GLM-5.2 on Blackhole
— not attempted —
Sim
— not attempted —
Sim
🥇GLM-5.2 on Fluid
— not attempted —
Sim
🥉GLM-5.2 on Fractal
— not attempted —
Sim
🥈GLM-5.2 on Orbit
— not attempted —
— not attempted —
— not attempted —
Visual
GLM-5.2 on Terrain
— not attempted —
Visual
🥇GLM-5.2 on Voxel
— not attempted —

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

Kimi K2.7 · Fast

Strengths

  • Lowest latency of the three Kimi modes for short builds
  • Same 256K context as Quality mode
  • Best when you need agent-loop responsiveness over polish

Trade-offs

  • Skips deeper reasoning passes — bronze-tier on tasks needing planning
  • Julian explicitly does not assign scores to Kimi modes on the standalone bench

Pricing & context — the spec sheet

Spec GLM-5.2 Kimi K2.7 · Fast
VendorZhipu / Z.aiMoonshot AI
Context window1,000,000 tokens256,000 tokens
PriceOpen weights · free for individualsFlat plan (no per-token bill)
Pricing detailOpen-weights release: weights downloadable from Hugging Face for self-hosting, or runnable for free on z.ai for individuals (commercial use has separate licensing).Same flat-rate plan as standard Kimi K2.7 — Fast mode is a runtime toggle, not a separate model.
Release2026-06-142026-06
Bench coverage13/21 scored · avg 8.23/100/3 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 GLM-5.2 and Kimi K2.7 · Fast 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, snappy iteration inside agent loops → Kimi K2.7 · Fast. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Kimi K2.7 · Fast

Which is better, GLM-5.2 or Kimi K2.7 · Fast?

On Goldie Bench, GLM-5.2 averages no scored verdicts yet across the shared tasks, with 6 gold, 4 silver, 3 bronze overall. Kimi K2.7 · Fast 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 Kimi K2.7 · Fast?

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). Kimi K2.7 · Fast: Same flat-rate plan as standard Kimi K2.7 — Fast mode is a runtime toggle, not a separate model.

What's the context window for GLM-5.2 vs Kimi K2.7 · Fast?

GLM-5.2 has a 1,000,000 tokens context window. Kimi K2.7 · Fast has a 256,000 tokens context window.

When should I pick GLM-5.2 over Kimi K2.7 · Fast?

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 Kimi K2.7 · Fast over GLM-5.2?

Pick Kimi K2.7 · Fast for: Snappy iteration inside agent loops; Short prompts where Quality mode would over-think; Live demos where latency matters more than the last 5% of polish. The trade-off is the weaknesses we logged on the bench: Skips deeper reasoning passes — bronze-tier on tasks needing planning; Julian explicitly does not assign scores to Kimi modes on the standalone bench.

How does Goldie Bench score GLM-5.2 vs Kimi K2.7 · Fast?

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