Fugu Mini vs Qwen 3.7

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 Fugu Mini and Qwen 3.7, side by side, on 37 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.

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

Qwen 3.7 · Wired alongside GLM-5.2 in Agent OS for open-weights agent loops where you want vendor diversity.

Where Fugu Mini beat Qwen 3.7

The tasks where I gave Fugu Mini a higher 0–10 score on the same prompt — with the actual commentary from my source guides.

Where Qwen 3.7 beat Fugu Mini

The tasks where I gave Qwen 3.7 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 36 scored shared tasks, Fugu Mini averaged 7.75/10, beating Qwen 3.7's 6.86/10 by 0.89 points. Pick Fugu Mini 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 Fugu Mini and Qwen 3.7 both into the Agent Operating System and dispatch each from the kanban by task type — agent loops where latency matters more than panel consensus → Fugu Mini, open-weights alternative to glm-5.2 when you want a different model family → Qwen 3.7. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Fugu Mini vs Qwen 3.7

Which is better, Fugu Mini or Qwen 3.7?

On Goldie Bench, Fugu Mini averages 7.75/10 across the shared tasks, with 2 gold, 0 silver, 1 bronze overall. Qwen 3.7 averages 6.86/10, with 0 gold, 0 silver, 0 bronze. Fugu Mini wins the head-to-head 29–3.

How much does Fugu Mini cost vs Qwen 3.7?

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. Qwen 3.7: Alibaba's open-weights release — downloadable from Hugging Face, runnable locally or via Alibaba Cloud's free tier for individuals.

What's the context window for Fugu Mini vs Qwen 3.7?

Fugu Mini has a Single-model variant of Sakana's Fugu — no panel orchestration. Same API endpoint, much faster per call. context window. Qwen 3.7 has a 256,000 tokens context window.

When should I pick Fugu Mini over Qwen 3.7?

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.

When should I pick Qwen 3.7 over Fugu Mini?

Pick Qwen 3.7 for: Open-weights alternative to GLM-5.2 when you want a different model family; Multilingual workloads (Chinese, multi-script content); Fluid and particle simulations. The trade-off is the weaknesses we logged on the bench: Only 5 tasks scored on the bench so far — small sample size; Trails GLM-5.2 on cinematic visual builds at similar pricing.

How does Goldie Bench score Fugu Mini vs Qwen 3.7?

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: Fugu Mini · Qwen 3.7 · back to the leaderboard