Last month, Sakana AI released their Fugu LLM, to headlines such as ‘Sakana AI launches Fugu Ultra and its orchestration model matches Fable and Mythos without training a single frontier model’ and ‘Sakana Fugu Ultra promises to deliver “the very best frontier-level performance” by… using frontier AI models.’ YouTube was full of videos asking ‘Is Fugu better than Fable?’
Early coverage framed it as “one model to command them all,” a way to match Anthropic’s Fable/Mythos-level results through collective intelligence rather than a single giant model, while also reducing dependence on any one provider or export-control risk. In short: multi-agent coordination packaged as a single model interface.
What may have gotten lost in the hype is what Fugu actually IS. Fugu is a family of “learned orchestrator models”. It is trained as a language model whose job is to read a query and dynamically construct an agentic scaffold (selecting workers, assigning roles, verifying outputs, synthesizing the answer) over a pool of frontier models. The user interacts with it as if it were a single model, but the heavy lifting is done by the orchestrated team.
You talk to it like any other model (OpenAI-compatible API), but under the hood it reads the query and dynamically builds the scaffold and pool of workers, deciding which models to call, what roles to give them, how to verify intermediate results, and when to synthesize the final answer.
There are two variants:
- Fugu – prioritizes everyday usability and lower latency; it often routes to a single strong worker.
- Fugu-Ultra – prioritizes maximum quality on hard, multi-step problems and can spin up richer multi-agent workflows with isolation and shared memory.
Of course, I had to add it to my autonomous agent game creation test.
I tried out “Ultra” because I didn’t know what to expect, and I wanted to give Fugu the greatest chance of creating a playable game.
Some results:
- Fugu Ultra ran for a LONG time, compared to even the most powerful “uni-models”. I didn’t expect this, and so I didn’t think to time the run, but it was probably an hour or more. This in comparison to strict Fable or Opus runs (~45-50 minutes) and Grok 4.5 (~20 minutes). Note that Fugu Ultra is designed to spin up multi-agent workflows to solve the problem, and can easily spin up multiple ‘heavy lift’ models that may take a long time to synchronize and complete the task.
- Fugu Ultra is EXPENSIVE, somewhere in the $70-$80 range to design and implement the prompted game in my test, similar to strict Fable or Opus. Again, it was likely spinning up expensive models to do a lot of the work. For some reason I had not expected this (duh!), and ended up feeding OpenRouter multiple $20 token buys to get the job done.
- Fugu Ultra is INTERESTING in what aesthetics and features it designed. The look and the play of the game it produced has a distinct style, so much so that the style can distract from gameplay; however, I like it in that its style is unique vs. the other LLMs that I’ve tested. For example, it chose to use what looks like swirling
confetti for its explosions instead of a more vector graphics look!
What’s really interesting to me is, I don’t know what LLM Fugu outsourced the aesthetics and game design decisions to, and it’s a really unique style. I’m not sure that a second run would recreate that same style. (I’d try a second run as an experiment if the token cost wasn’t so high)
The runable (browser based) version of the Fugu Ultra game (as well as the games from the open model LLMs) can be found here (arrow keys to turn and thrust, space to fire, h for hyperspace):
https://rgm-llm-asteroids-openweight-arcade.netlify.app
Honestly, although there are some somewhat annoying things about the game implementation, I really do love the confetti explosions.
Sakana Fugu’s technical report : https://arxiv.org/html/2606.21228v2
