GLM-5.2 vs Fugu Mini

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 Fugu Mini, side by side, on 16 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.

Fugu Mini · Dispatched from Agent OS as the fast Sakana lane. Bench scored by Claude judge against the same 42 prompts.

Where GLM-5.2 beat Fugu Mini

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.

Strengths & weaknesses I logged

Pricing & context — the spec sheet

The verdict — which should you pick?

Across 2 scored shared tasks, GLM-5.2 averaged 8.75/10, beating Fugu Mini's 5.50/10 by 3.25 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 Fugu Mini 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, agent loops where latency matters more than panel consensus → Fugu Mini. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Fugu Mini

Which is better, GLM-5.2 or Fugu Mini?

On Goldie Bench, GLM-5.2 averages 8.75/10 across the shared tasks, with 5 gold, 1 silver, 2 bronze overall. Fugu Mini averages 5.50/10, with 0 gold, 0 silver, 0 bronze. GLM-5.2 wins the head-to-head 2–0.

How much does GLM-5.2 cost vs Fugu Mini?

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). Fugu Mini: The non-Ultra `fugu` model on Sakana's API. Sakana describes it as 'Fast mini model optimized for low latency yet high quality responses.' Crucially: zero orchestration tokens per call (vs Ultra's panel of thousands). Returns in ~3 min instead of 6-15 min and doesn't time out on heavy prompts.

What's the context window for GLM-5.2 vs Fugu Mini?

GLM-5.2 has a 1,000,000 tokens context window. Fugu Mini has a Single-model variant of Sakana's Fugu — no panel orchestration. Same API endpoint, much faster per call. context window.

When should I pick GLM-5.2 over Fugu Mini?

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 Fugu Mini over GLM-5.2?

Pick Fugu Mini for: Agent loops where latency matters more than panel consensus; Quick first-drafts you'll refine downstream; Filling out a bench when Ultra is timing out. The trade-off is the weaknesses we logged on the bench: Single model only — no ensemble verdict; Newer than Ultra — less calibration / verification.

How does Goldie Bench score GLM-5.2 vs Fugu Mini?

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:

Full model pages: GLM-5.2 · Fugu Mini · back to the leaderboard