
Real head-to-head · same prompt, one shot
MiniMax M3 vs Fugu Ultra 1.1
1M-context frontier model at $0.30/M tokens — cheapest big-context model on the bench. vs Sakana's multi-agent orchestrator, v1.1 — routes experts per request.
Head-to-head verdict: MiniMax M3 wins 17–5 with 1 tie.
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 MiniMax M3 and Fugu Ultra 1.1, side by side, on 24 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.
MiniMax M3 · Bench prompts dispatched via OpenRouter. Scored by Claude judge against the same 42 prompts every other model ran.
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 ↓
MiniMax M3
Fugu Ultra 1.1
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Where MiniMax M3 beat Fugu Ultra 1.1
The tasks where I gave MiniMax M3 a higher 0–10 score on the same prompt — with the actual commentary from my source guides.
Neoncity
Game
MiniMax M3 7.5
·
Fugu Ultra 1.1 2.3
(+5.2)
What I saw: Cyberpunk flythrough with neon towers + light trails.
Raycaster
Game
MiniMax M3 7.0
·
Fugu Ultra 1.1 2.0
(+5.0)
What I saw: Canvas-2D raycaster — WASD walking, textured walls, distance fog.
Nordiccrypt
Game
MiniMax M3 9.0
·
Fugu Ultra 1.1 5.2
(+3.8)
What I saw: 41KB Nordic crypt with torch-lit corridors, chasing skeletons, boss room.
Pool
Game
MiniMax M3 7.5
·
Fugu Ultra 1.1 4.5
(+3.0)
What I saw: Canvas-2D billiards with 16 balls, pockets, click-drag aim.
Arcade
Game
MiniMax M3 8.0
·
Fugu Ultra 1.1 5.5
(+2.5)
What I saw: Neon Breakout — paddle, ball, brick wall, particle trails, score HUD.
Where Fugu Ultra 1.1 beat MiniMax M3
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.
Outrun
Game
Fugu Ultra 1.1 8.6
·
MiniMax M3 7.5
(+1.1)
· neon combat runner
What I saw: Gorgeous synthwave city with pseudo-3D road, glowing hero craft on a contact disc, and a visible enemy vehicle ahead with combat HUD (KILLS, THREAT HIGH, crosshair) — clearly a combat runner not an empty walking sim. Polished vignette, meters, and banner elevate it above the fiel…
Parachute
Game
Fugu Ultra 1.1 8.6
·
MiniMax M3 8.0
(+0.6)
· combat parachute drop
What I saw: Strong deployed-chute skydiver over a jungle canopy with a polished HUD (altitude, dist-to-H, score) plus active drone enemies, flare combat, and 'THREAT DOWN' kill feedback — it delivers the full jump/steer/land loop AND working combat, edging past a bland walking sim.
Doom
Game
Fugu Ultra 1.1 8.4
·
MiniMax M3 8.0
(+0.4)
· 3D demon shooter
What I saw: Strong atmospheric 3D maze with a visible horned demon enemy, working shotgun, minimap with tracked enemies, and active combat ('CLAWED' hit feedback, HP dropped to 082) — polished HUD and lighting. Falls just shy of the field's best; enemies are more Three.js models than true ra…
Flightsim
Game
Fugu Ultra 1.1 8.4
·
MiniMax M3 8.0
(+0.4)
What I saw: Polished flight sim with a detailed aircraft model, full HUD (airspeed/alt/VS/heading tape/attitude indicator), runway with markings, hangar, control tower and terrain — plus a combat layer with visible drones and cannon reticle. Very strong and shippable, but the enemy at this f…
Rpg
Game
Fugu Ultra 1.1 8.4
·
MiniMax M3 8.0
(+0.4)
· Verdant Relic RPG
What I saw: Polished top-down 3D RPG with visible enemies in aggro rings, active combat ('HIT -13' damage numbers, health at 87), pickups, a shrine objective, and a clean HUD showing inventory/kills/hostiles; strong shippable build, only slightly held back by the inventory system being minim…
Strengths & weaknesses I logged
MiniMax M3
Strengths
- 1M token context — full repo / full deep-research corpus fits in one call
- $0.30/M input is roughly 1/30th of Opus 4.8 — built for high-volume agent loops
- Solid one-shot HTML output — clean structure on game and visual prompts
Trade-offs
- Less polished than Fusion's panel-ensembled output on the toughest deep builds
- Newer model — less community calibration vs Fable 5 / Opus 4.8
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 | MiniMax M3 | Fugu Ultra 1.1 |
|---|---|---|
| Vendor | MiniMax | Sakana AI |
| Context window | 1,048,576-token context — matches GLM-5.2 and Fable 5 | 1,000,000-token context window |
| Price | $0.30 / 1M input tokens, $1.50 / 1M output | API · orchestration billed |
| Pricing detail | MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight. | 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. |
| Release | 2026-06-18 | 2026-07 |
| Bench coverage | 47/47 scored · avg 7.97/10 | 23/24 scored · avg 6.94/10 |
The verdict — which should you pick?
Across 23 scored shared tasks, MiniMax M3 averaged 8.15/10, beating Fugu Ultra 1.1's 6.94/10 by 1.21 points. Pick MiniMax M3 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 MiniMax M3 and Fugu Ultra 1.1 both into the Agent Operating System and dispatch each from the kanban by task type — high-volume agent workflows where per-call cost dominates → MiniMax M3, 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 — MiniMax M3 vs Fugu Ultra 1.1
Which is better, MiniMax M3 or Fugu Ultra 1.1?
On Goldie Bench, MiniMax M3 averages 8.15/10 across the shared tasks, with 2 gold, 1 silver, 4 bronze overall. Fugu Ultra 1.1 averages 6.94/10, with 0 gold, 1 silver, 2 bronze. MiniMax M3 wins the head-to-head 17–5.
How much does MiniMax M3 cost vs Fugu Ultra 1.1?
MiniMax M3: MiniMax M3 is the cheapest 1M-context frontier model on the bench — roughly 1/200th the per-call cost of OpenRouter Fusion and 1/30th of Claude Opus 4.8. Designed for high-volume agent workloads where context length matters but per-call budget is tight. 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 MiniMax M3 vs Fugu Ultra 1.1?
MiniMax M3 has a 1,048,576-token context — matches GLM-5.2 and Fable 5 context window. Fugu Ultra 1.1 has a 1,000,000-token context window context window.
When should I pick MiniMax M3 over Fugu Ultra 1.1?
Pick MiniMax M3 for: High-volume agent workflows where per-call cost dominates; 1M-context tasks (whole-repo refactors, deep-research synthesis); Drop-in cheaper alternative to GLM-5.2 with comparable 1M context. The trade-off is the weaknesses we logged on the bench: Less polished than Fusion's panel-ensembled output on the toughest deep builds; Newer model — less community calibration vs Fable 5 / Opus 4.8.
When should I pick Fugu Ultra 1.1 over MiniMax M3?
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 MiniMax M3 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.
Related comparisons
Other head-to-heads using the same scoring system:
MiniMax M3 vs Fusion Fugu Ultra 1.1 vs Fusion MiniMax M3 vs Claude Opus 5 Fugu Ultra 1.1 vs Claude Opus 5 MiniMax M3 vs Hermes MoA Fugu Ultra 1.1 vs Hermes MoA MiniMax M3 vs GPT-5.6 Sol Fugu Ultra 1.1 vs GPT-5.6 SolFull model pages: MiniMax M3 · Fugu Ultra 1.1 · 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 4,000+ founders shipping with it every day all live inside the AI Profit Boardroom.
4,000+founders
258documented wins
38countries
$59/momonthly














































