Looks like Fusion calls a bunch of models and then uses an LLM to synthesize the results, and pass to another model for final output.

Fugu looks like it's doing something different? Using an LLM earlier on in the flow as an orchestrator to decide which other LLMs to call. More coordinator than simply synthesizing results, and more "agentic".

It's interesting because it's all exposed behind a single OpenAI compatible endpoint (Responses API?) and so then presumably someone could use this for one of their single agents. Now you have agent-of-agents, nested in some sense. The token usage increases accordingly!

We open sourced it all

and will be releasing a similar orchestrator next week on TrustedRouter

This is ask a special orchestrator they built, which is in front of a bunch of models, which model would suit the request best.

Regular Fugu seems to be just "pick the best model and route the request there"

Fugu Ultra can generate like a little mini workflow/plan instead to achieve a result

1. Ask GPT to derive the math. 2. Ask Opus to check for implementation/security issues. 3. Ask Gemini to synthesize or resolve disagreement. 4. Return final answer.

I could be wrong but seems to be that at a glance, so I think it's more dynamic than OpenRouter Fusion.

> So basically... openrouter

:skull:

i now really wonder how many people of the public understood my thesis defense lol

Is there any official source that could confirms if Fable (or Mythos) is parallelized test-time compute (like GPT 5.5 Pro) or sparse Mixture-of-Experts (MoE) transformer combined with a multi-agent, inference-time compute scaling architecture (Gemini 3.1 Deep Think)?

Basically, if you combine a bunch of near-frontier models (like GPT 5.5, etc) you can get performance that sometimes surpasses top line models like Claude's Fable.

Sakana seems to have a separate approach using a domain specific model to perform the model routing step.

(don't send anything, sharing only because of the base58 fun fact I didn't know)