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Real head-to-head · same prompt, one shot

Opus 4.8 vs Fugu Ultra 1.1

The reasoning king — deepest thinking, premium price. vs Sakana's multi-agent orchestrator, v1.1 — routes experts per request.

Opus 4.8 · context200K tokens
Fugu Ultra 1.1 · context1M tokens
Opus 4.8 · price$15 / $75 per M tokens
Fugu Ultra 1.1 · priceAPI · orchestration billed
Opus 4.8 · vendorAnthropic
Fugu Ultra 1.1 · vendorSakana AI

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 Opus 4.8 and Fugu Ultra 1.1, side by side, on 0 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.

Opus 4.8 · The default when the build has to ship on the first prompt — Opus is the safety net inside Agent OS for hard one-shots.

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.

Side-by-side on 47 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 ↓
Opus 4.8
Fugu Ultra 1.1
Game
🥉Opus 4.8 on Arcade
— not attempted —
Game
Opus 4.8 on Crypt
— not attempted —
Game
Opus 4.8 on Dogfight
— not attempted —
Game
🥈Opus 4.8 on Doom
— not attempted —
Opus 4.8 on Dragonflight
— not attempted —
Opus 4.8 on Dragonrealm
— not attempted —
Game
Opus 4.8 on Flightsim
— not attempted —
Game
Opus 4.8 on Game
— not attempted —
Game
Opus 4.8 on Gtadrive
— not attempted —
Game
Opus 4.8 on Gtafoot
— not attempted —
Opus 4.8 on Neonblaster
— not attempted —
Game
Opus 4.8 on Neoncity
— not attempted —
Game
Opus 4.8 on Neonracer
— not attempted —
Opus 4.8 on Nordiccrypt
— not attempted —
Game
Opus 4.8 on Outrun
— not attempted —
Game
Opus 4.8 on Parachute
— not attempted —
Game
Opus 4.8 on Pool
— not attempted —
Game
Opus 4.8 on Racing
— not attempted —
Game
Opus 4.8 on Raycaster
— not attempted —
Game
Opus 4.8 on Rpg
— not attempted —
Game
Opus 4.8 on Skyrim
— not attempted —
Opus 4.8 on Twilightvale
— not attempted —
Game
Opus 4.8 on Voxelcraft
— not attempted —
Page
Opus 4.8 on Aipbpromo
— not attempted —

Strengths & weaknesses I logged

Opus 4.8

Strengths

  • Most consistent across the Goldie Bench bench — no weak build, 8.46/10 average
  • Deepest one-shot reasoning, especially on game-feel and physics
  • Extended thinking mode handles up to 1M tokens of context

Trade-offs

  • 5–10× the per-token cost of every other model on the bench
  • Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments

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

Pricing & context — the spec sheet

Spec Opus 4.8 Fugu Ultra 1.1
VendorAnthropicSakana AI
Context window200,000 tokens (1M with extended thinking)1,000,000-token context window
Price$15 / $75 per M tokensAPI · orchestration billed
Pricing detailPremium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency.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.
Release2026-052026-07
Bench coverage47/47 scored · avg 7.51/100/0 scored · avg —

The verdict — which should you pick?

Not enough scored shared tasks yet for a head-to-head average. The live demos for both are on the matrix above — play them and form your own opinion.

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 Opus 4.8 and Fugu Ultra 1.1 both into the Agent Operating System and dispatch each from the kanban by task type — mission-critical one-shot builds where 'has to work the first time' matters → Opus 4.8, 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 — Opus 4.8 vs Fugu Ultra 1.1

Which is better, Opus 4.8 or Fugu Ultra 1.1?

On Goldie Bench, Opus 4.8 averages no scored verdicts yet across the shared tasks, with 3 gold, 1 silver, 1 bronze overall. Fugu Ultra 1.1 averages no scored verdicts yet, with 0 gold, 0 silver, 0 bronze. Not enough scored shared tasks yet to call a winner.

How much does Opus 4.8 cost vs Fugu Ultra 1.1?

Opus 4.8: Premium pricing via the Anthropic API: $15 per million input tokens, $75 per million output tokens. Extended thinking is included but adds latency. 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 Opus 4.8 vs Fugu Ultra 1.1?

Opus 4.8 has a 200,000 tokens (1M with extended thinking) context window. Fugu Ultra 1.1 has a 1,000,000-token context window context window.

When should I pick Opus 4.8 over Fugu Ultra 1.1?

Pick Opus 4.8 for: Mission-critical one-shot builds where 'has to work the first time' matters; Hard reasoning tasks (planning, multi-step) where you'll pay for the depth; Anything where vendor reliability beats the per-token bill. The trade-off is the weaknesses we logged on the bench: 5–10× the per-token cost of every other model on the bench; Less flair on cinematic visuals than GLM-5.2 — playing it safer wins on accuracy, costs you on showpiece moments.

When should I pick Fugu Ultra 1.1 over Opus 4.8?

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 Opus 4.8 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.

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 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.

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