GLM-5.2 vs Fugu Ultra 1.1

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 Ultra 1.1, side by side, on 24 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 Ultra 1.1 · Benched on GoldieBench via Sakana's Responses API (fugu-ultra-v1.1, xhigh reasoning). Game tasks use the skill-infused threejs-game-director prompt plus a controls+graphics fix loop with an anti-regression clamp, judged on a real mid-play frame by the same Opus judge as the field.

Where GLM-5.2 beat Fugu Ultra 1.1

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.

Where Fugu Ultra 1.1 beat GLM-5.2

The tasks where I gave Fugu Ultra 1.1 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 23 scored shared tasks, GLM-5.2 averaged 7.72/10, beating Fugu Ultra 1.1's 6.94/10 by 0.77 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 Ultra 1.1 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, hard, high-stakes coding and reasoning where answer quality beats latency → Fugu Ultra 1.1. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — GLM-5.2 vs Fugu Ultra 1.1

Which is better, GLM-5.2 or Fugu Ultra 1.1?

On Goldie Bench, GLM-5.2 averages 7.72/10 across the shared tasks, with 5 gold, 0 silver, 0 bronze overall. Fugu Ultra 1.1 averages 6.94/10, with 0 gold, 1 silver, 2 bronze. GLM-5.2 wins the head-to-head 12–11.

How much does GLM-5.2 cost vs Fugu Ultra 1.1?

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 Ultra 1.1: Sakana's Fugu Ultra v1.1 — a multi-agent system served as a model: an orchestrator routes each request across one to three expert agents and synthesises the answer. Reported (v1.0): SWE-Bench Pro 73.7, LiveCodeBench 93.2, GPQA Diamond 95.5. NOTE: Sakana geo-blocks the EU/EEA/UK/Switzerland pending GDPR compliance — benched from an allowed region.

What's the context window for GLM-5.2 vs Fugu Ultra 1.1?

GLM-5.2 has a 1,000,000 tokens context window. Fugu Ultra 1.1 has a 1,000,000-token context window context window.

When should I pick GLM-5.2 over Fugu Ultra 1.1?

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 Ultra 1.1 over GLM-5.2?

Pick Fugu Ultra 1.1 for: Hard, high-stakes coding and reasoning where answer quality beats latency; Agentic workflows in Codex / Claude Code via the drop-in provider config; One-shot builds you want a panel of experts on, not a single model. The trade-off is the weaknesses we logged on the bench: Benched as a partial run until the full 50-task batch completes; Region-locked: unavailable across the EU/EEA/UK/CH — access depends on where you are.

How does Goldie Bench score GLM-5.2 vs Fugu Ultra 1.1?

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 Ultra 1.1 · back to the leaderboard