
Real head-to-head · same prompt, one shot
Fugu Mini vs LongCat-2.0
Fugu's fast mini variant — single model, no panel, ~3 min per build. vs The open 1.6T MoE that builds — a frontier coder trained on non-Nvidia ASIC superpods.
Head-to-head verdict: LongCat-2.0 wins 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 Mini and LongCat-2.0, side by side, on 2 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.
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.
Side-by-side on 39 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 ↓
Fugu Mini
LongCat-2.0
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Where LongCat-2.0 beat Fugu Mini
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.
Dragonrealm
Game
LongCat-2.0 8.5
·
Fugu Mini 8.0
(+0.5)
What I saw: One-shot 15KB three.js snow open-world — snow-capped mountains + 30 low-poly pines, 3000-particle falling snow, first-person glowing sword, fog. Real WASD+mouse+sprint controls, terrain-follow. verify-move: walks+looks, canvas 1440x810, 0 errors. Flawless first try — no patch.
Skyrim
Game
LongCat-2.0 8.5
·
Fugu Mini 8.0
(+0.5)
What I saw: One-shot 23KB open-world explorer (the richest of the four) — rolling displaced terrain, snow mountains, a stone watchtower, 20+ conifers, boulders, grass, clouds, and terrain-height following. Real WASD+mouse. verify-move: walks+looks, 0 errors.
Strengths & weaknesses I logged
Fugu Mini
Strengths
- Zero panel orchestration — much lower latency than Ultra
- Same Sakana subscription, no extra cost
- Doesn't time out on heavy game/3D prompts where Ultra stalls
Trade-offs
- Single model only — no ensemble verdict
- Newer than Ultra — less calibration / verification
LongCat-2.0
Strengths
- One-shot GoldieBench: 3 of 4 flawless playable 3D builds (Dragon Realm 8.5, Skyrim 8.5, Crypt 8.0); Voxel Craft built one-shot but needed a 1-line camera fix (7.5) — avg 8.1
- 1.6T-param MoE (~48B active/token) with LongCat Sparse Attention + a 1M-token window — built for long-horizon agentic + coding tasks
- Open weights, deeply integrated with Claude Code, OpenClaw and Hermes — a free frontier-class coder to slot into the Agent OS
Trade-offs
- 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
Pricing & context — the spec sheet
| Spec | Fugu Mini | LongCat-2.0 |
|---|---|---|
| Vendor | Sakana AI | Meituan |
| Context window | Single-model variant of Sakana's Fugu — no panel orchestration. Same API endpoint, much faster per call. | 1,000,000 tokens (LongCat Sparse Attention) |
| Price | Same Sakana subscription pool as Fugu Ultra | Open weights · free web chat · API |
| Pricing detail | 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. | 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. |
| Release | 2026-06-15 | 2026-06 |
| Bench coverage | 36/37 scored · avg 7.75/10 | 4/4 scored · avg 8.12/10 |
The verdict — which should you pick?
Across 2 scored shared tasks, LongCat-2.0 averaged 8.50/10, beating Fugu Mini's 8.00/10 by 0.50 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 Mini and LongCat-2.0 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, one-shot single-file 3d / html / game builds inside the agent os → LongCat-2.0. That's the same setup I run for the 3,600+ founders inside the AI Profit Boardroom.
FAQ — Fugu Mini vs LongCat-2.0
Which is better, Fugu Mini or LongCat-2.0?
On Goldie Bench, Fugu Mini averages 8.00/10 across the shared tasks, with 2 gold, 5 silver, 3 bronze overall. LongCat-2.0 averages 8.50/10, with 0 gold, 1 silver, 2 bronze. LongCat-2.0 wins the head-to-head 2–0.
How much does Fugu Mini cost vs LongCat-2.0?
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. 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 Mini vs LongCat-2.0?
Fugu Mini has a Single-model variant of Sakana's Fugu — no panel orchestration. Same API endpoint, much faster per call. context window. LongCat-2.0 has a 1,000,000 tokens (LongCat Sparse Attention) context window.
When should I pick Fugu Mini over LongCat-2.0?
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 LongCat-2.0 over Fugu Mini?
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 Mini 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:
Fugu Mini vs Fusion LongCat-2.0 vs Fusion Fugu Mini vs Hermes MoA LongCat-2.0 vs Hermes MoA Fugu Mini vs Grok LongCat-2.0 vs Grok Fugu Mini vs MiniMax M3 LongCat-2.0 vs MiniMax M3Full model pages: Fugu Mini · LongCat-2.0 · 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
$59/momonthly
























