
Fugu Ultra 1.1 vs Hy3
Sakana's multi-agent orchestrator, v1.1 — routes experts per request. vs Tencent's open-weights coder — Apache-2.0, cheap, beats GLM-5.1 on frontend in Tencent's blind eval.
Head-to-head verdict: Fugu Ultra 1.1 wins 1–0.
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 Hy3, side by side, on 1 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.
Hy3 · Wired into the Agent OS as the 'Hy3 Coder' tab (chat + live preview + workspace) via OpenRouter. Bench built one-shot on the same prompts as the field; weak builds iterated by Hy3 itself (the model fixes its own builds).
Side-by-side on 7 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 = 🥉).
Where Fugu Ultra 1.1 beat Hy3
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.
What I saw: Strong Skyrim-vibe frozen open world with layered snowy mountains, a player with visible sword, multiple approaching enemies with health orbs, runes, an event banner ('DRAGON SWOOP · FIRE BREATH') and polished HUD/compass — clearly beats the empty-walking-sim trap. Enemy models a…
Strengths & weaknesses I logged
Fugu Ultra 1.1
Strengths
- Orchestrates 1-3 expert agents per request and synthesises their answers
- Reported SWE-Bench Pro 73.7 — above Opus 4.8 and GPT-5.5 on Sakana's table
- OpenAI- and Anthropic-compatible API — drop-in for Codex and Claude Code
Trade-offs
- 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
Hy3
Strengths
- Apache-2.0 open weights — self-host free, no lock-in
- Tencent's 270-expert blind eval: 2.67/4 vs GLM-5.1's 2.51, strongest on frontend / data / CI-CD
- Hallucination rate cut 12.5% → 5.4%; stable tool-calls across scaffoldings (<4% SWE-Bench variance)
Trade-offs
- Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops
- One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass
Pricing & context — the spec sheet
| Spec | Fugu Ultra 1.1 | Hy3 |
|---|---|---|
| Vendor | Sakana AI | Tencent Hunyuan |
| Context window | 1,000,000-token context window | 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. |
| Price | API · orchestration billed | $0.14 / 1M input · $0.58 / 1M output |
| Pricing detail | 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. | Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible. |
| Release | 2026-07 | 2026-07-06 |
| Bench coverage | 1/1 scored · avg 8.60/10 | 7/7 scored · avg 6.76/10 |
The verdict — which should you pick?
Across 1 scored shared tasks, Fugu Ultra 1.1 averaged 8.60/10, beating Hy3's 7.20/10 by 1.40 points. Pick Fugu Ultra 1.1 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 Hy3 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, cost-sensitive coding + frontend design where open weights matter → Hy3. That's the same setup I run for the 4,000+ founders inside the AI Profit Boardroom.
FAQ — Fugu Ultra 1.1 vs Hy3
Which is better, Fugu Ultra 1.1 or Hy3?
On Goldie Bench, Fugu Ultra 1.1 averages 8.60/10 across the shared tasks, with 0 gold, 0 silver, 1 bronze overall. Hy3 averages 7.20/10, with 0 gold, 0 silver, 0 bronze. Fugu Ultra 1.1 wins the head-to-head 1–0.
How much does Fugu Ultra 1.1 cost vs Hy3?
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. Hy3: Tencent Hunyuan 3 — open-weights under Apache-2.0, so free to self-host. On OpenRouter it is one of the cheapest capable coders: ~$0.14/M in, $0.58/M out (1 RMB / 4 RMB). Upstream can be slow (30-90s to first token), but per-token cost is negligible.
What's the context window for Fugu Ultra 1.1 vs Hy3?
Fugu Ultra 1.1 has a 1,000,000-token context window context window. Hy3 has a 262,144-token context window. Open weights (Apache-2.0) on HuggingFace / ModelScope / GitHub; benched here via OpenRouter. context window.
When should I pick Fugu Ultra 1.1 over Hy3?
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 Hy3 over Fugu Ultra 1.1?
Pick Hy3 for: Cost-sensitive coding + frontend design where open weights matter; Self-hosters who want an Apache-2.0 model they fully own; Anyone wiring a cheap capable coder into a live build panel (Agent OS Hy3 Coder tab). The trade-off is the weaknesses we logged on the bench: Slow upstream on OpenRouter (30-90s per build) — fine for one-shots, sluggish for tight loops; One-shot game builds can under-render (flat raycaster walls, unlit 3D) without an iterate pass.
How does Goldie Bench score Fugu Ultra 1.1 vs Hy3?
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 Ultra 1.1 vs Fusion Hy3 vs Fusion Fugu Ultra 1.1 vs Claude Opus 5 Hy3 vs Claude Opus 5 Fugu Ultra 1.1 vs Hermes MoA Hy3 vs Hermes MoA Fugu Ultra 1.1 vs GPT-5.6 Sol Hy3 vs GPT-5.6 SolFull model pages: Fugu Ultra 1.1 · Hy3 · back to the leaderboard
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 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.






