Real head-to-head · same prompt, one shot
GLM-5.2 vs MiniMax M3
The never-forgets agent — 1M context, open weights. vs 1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench.
Head-to-head verdict: GLM-5.2 wins 8–3 with 2 ties.
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 MiniMax M3, side by side, on 31 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.
MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.
Side-by-side on 42 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
MiniMax M3
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Game
Page
Page
Sim
Sim
Sim
Sim
Sim
Sim
Where GLM-5.2 beat MiniMax M3
The tasks where I gave GLM-5.2 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Fluid
Sim
GLM-5.2 9.0
·
MiniMax M3 7.5
(+1.5)
· winner · best liquid
What I saw: GLM filled the bowl with glowing liquid that actually sloshes — the most convincing 'liquid in a bowl'. Opus's particles glowed but clumped to the centre. Kimi's collapsed into a tiny blob.
Neoncity
Game
GLM-5.2 9.0
·
MiniMax M3 7.5
(+1.5)
· winner · cinematic
What I saw: GLM's is the most cinematic — neon towers, a setting sun, Japanese signage and a flight HUD, like a frame from a film. Opus's is a clean canyon of lit skyscrapers racing to a vanishing point. Kimi leaned into the synthwave sun and grid more than the city itself. GLM wins the skyline.
Blackhole
Sim
GLM-5.2 8.0
·
MiniMax M3 7.0
(+1.0)
What I saw: Opus nailed it — a pure-black event horizon, a bright photon ring, and the disk bent up and over the top exactly like the film's lensing. GLM came in strong with a clean ring and a starfield warping past the hole. Kimi's disk is fine, but the background is a soft grey blur instea…
Solar
Sim
GLM-5.2 8.5
·
MiniMax M3 7.5
(+1.0)
What I saw: Three genuinely good space sims. Opus tilts the orbits into real 3D with a bloom-heavy sun and Saturn's rings. GLM's is the most product-like — labelled planets, orbit and label toggles, a clean HUD. Kimi's is a tidy tilted-orbit system with rings and a deep starfield. Opus and G…
Landing
Page
GLM-5.2 9.0
·
MiniMax M3 8.0
(+1.0)
· tie · top
What I saw: Funniest result of the lot: GLM and Opus independently produced near-identical premium 'Introducing Nova 1 — Intelligence, reimagined / distilled' keynote pages — gradient hero, full nav, pricing tiers. A dead heat. Kimi's was a plainer set of feature cards.
Where MiniMax M3 beat GLM-5.2
The tasks where I gave MiniMax M3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Orbit
Sim
MiniMax M3 8.5
·
GLM-5.2 7.5
(+1.0)
What I saw: 44KB top-down orbit map — Mercury through Mars with accurate relative speeds, hover info cards.
Fractal
Sim
MiniMax M3 8.5
·
GLM-5.2 8.0
(+0.5)
What I saw: WebGL Mandelbrot shader with click-to-zoom, hold-to-continuous-zoom.
Raycaster
Game
MiniMax M3 7.0
·
GLM-5.2 6.5
(+0.5)
What I saw: Canvas-2D raycaster — WASD walking, textured walls, distance fog.
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
MiniMax M3
Strengths
- 1M token context — full repo / full deep-research corpus fits in one call
- $0.30/M input is roughly 1/30th of Opus 4.8 — built for high-volume agent loops
- Solid one-shot HTML output — clean structure on game and visual prompts
Trade-offs
- Less polished than Fusion's panel-ensembled output on the toughest deep builds
- Newer model — less community calibration vs Fable 5 / Opus 4.8
Pricing & context — the spec sheet
| Spec | GLM-5.2 | MiniMax M3 |
|---|---|---|
| Vendor | Zhipu / Z.ai | MiniMax |
| Context window | 1,000,000 tokens | 1,048,576-token context — matches GLM-5.2 and Fable 5 |
| Price | Open weights · free for individuals | $0.30 / 1M input tokens, $1.50 / 1M output |
| 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). | MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight. |
| Release | 2026-06-14 | 2026-06-18 |
| Bench coverage | 13/31 scored · avg 8.23/10 | 42/42 scored · avg 7.96/10 |
The verdict — which should you pick?
Across 13 scored shared tasks, GLM-5.2 averaged 8.23/10, beating MiniMax M3's 7.73/10 by 0.50 points. Pick GLM-5.2 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 GLM-5.2 and MiniMax M3 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, high-volume agent workflows where per-call cost dominates → MiniMax M3. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — GLM-5.2 vs MiniMax M3
Which is better, GLM-5.2 or MiniMax M3?
On Goldie Bench, GLM-5.2 averages 8.23/10 across the shared tasks, with 5 gold, 1 silver, 2 bronze overall. MiniMax M3 averages 7.73/10, with 12 gold, 11 silver, 8 bronze. GLM-5.2 wins the head-to-head 8–3.
How much does GLM-5.2 cost vs MiniMax M3?
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). MiniMax M3: MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight.
What's the context window for GLM-5.2 vs MiniMax M3?
GLM-5.2 has a 1,000,000 tokens context window. MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window.
When should I pick GLM-5.2 over MiniMax M3?
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 MiniMax M3 over GLM-5.2?
Pick MiniMax M3 for: High-volume agent workflows where per-call cost dominates; 1M-context tasks (whole-repo refactors, deep-research synthesis); Drop-in cheaper alternative to GLM-5.2 with comparable 1M context. The trade-off is the weaknesses we logged on the bench: Less polished than Fusion's panel-ensembled output on the toughest deep builds; Newer model — less community calibration vs Fable 5 / Opus 4.8.
How does Goldie Bench score GLM-5.2 vs MiniMax M3?
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 MiniMax M3 vs Opus 4.8 GLM-5.2 vs Grok MiniMax M3 vs Grok GLM-5.2 vs Fusion MiniMax M3 vs Fusion GLM-5.2 vs Fugu Ultra MiniMax M3 vs Fugu UltraFull model pages: GLM-5.2 · MiniMax M3 · 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