Fugu Ultra 1.1 vs LongCat-2.0

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 Ultra 1.1 and LongCat-2.0, side by side, on 3 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 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.

LongCat-2.0 · Run through the free longcat.chat web chat (the API key had no token quota), driven with the local-model-tester GoldieBench prompts; every build render-verified + playtested (verify-move.js: walks + looks + zero errors) before scoring. Slots into the Agent OS as an open frontier coder via its OpenAI-compatible API or the Claude Code / OpenClaw / Hermes harnesses.

Where Fugu Ultra 1.1 beat LongCat-2.0

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.

Where LongCat-2.0 beat Fugu Ultra 1.1

The tasks where I gave LongCat-2.0 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 3 scored shared tasks, LongCat-2.0 averaged 8.33/10, beating Fugu Ultra 1.1's 7.73/10 by 0.60 points. Pick LongCat-2.0 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 Ultra 1.1 and LongCat-2.0 both into the Agent Operating System and dispatch each from the kanban by task type — hard, high-stakes coding and reasoning where answer quality beats latency → Fugu Ultra 1.1, one-shot single-file 3d / html / game builds inside the agent os → LongCat-2.0. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.

FAQ — Fugu Ultra 1.1 vs LongCat-2.0

Which is better, Fugu Ultra 1.1 or LongCat-2.0?

On Goldie Bench, Fugu Ultra 1.1 averages 7.73/10 across the shared tasks, with 0 gold, 1 silver, 2 bronze overall. LongCat-2.0 averages 8.33/10, with 0 gold, 0 silver, 0 bronze. LongCat-2.0 wins the head-to-head 2–1.

How much does Fugu Ultra 1.1 cost vs LongCat-2.0?

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. LongCat-2.0: LongCat-2.0 is open-sourced (weights on Hugging Face + GitHub) and served via the longcat.chat web chat plus an OpenAI-compatible API (model id 'LongCat-2.0' at api.longcat.chat/openai/v1). It's a 1.6T-parameter MoE with ~48B activated per token, trained entirely on AI ASIC superpods (>50K accelerators, 35T+ tokens, no rollbacks). Note: the direct API key we were handed shipped with zero token quota ('Token 额度不足'), so every build here was run through the free web chat. Vendor: Meituan.

What's the context window for Fugu Ultra 1.1 vs LongCat-2.0?

Fugu Ultra 1.1 has a 1,000,000-token context window context window. LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window.

When should I pick Fugu Ultra 1.1 over LongCat-2.0?

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.

When should I pick LongCat-2.0 over Fugu Ultra 1.1?

Pick LongCat-2.0 for: One-shot single-file 3D / HTML / game builds inside the Agent OS; Long-context, repo-level edits + automated agentic task execution; A free, open, frontier-class coder to drop into the Model-Proof System. The trade-off is the weaknesses we logged on the bench: The direct API key we were given had near-zero token quota, so we ran it through the free web chat rather than the API; One camera-framing miss: Voxel Craft loaded facing away from the world (sky-only) until a one-line yaw/pitch patch pointed it at the terrain.

How does Goldie Bench score Fugu Ultra 1.1 vs LongCat-2.0?

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 Ultra 1.1 · LongCat-2.0 · back to the leaderboard