You pay $200/month to Anthropic, $200/month to OpenAI, $200/month to Cursor, $200/month to $200/month to Google, and seeing that it didn't come to a nice round $1024/month, you pay $200/month to Sakana to coordinate it all, because why not.
While you're at it, feel free to send me $200 as well, I'll generate a crypto address ending with "AI".
TIL: I just found out that base58 disallows I (capital i), l (lowercase L), O (capital o) and 0 (zero), so I could only generate GrxoJt4eNXE2QaQ55iPSa7hhiYdzCo8ZeAuokmh2Cai.
(don't send anything, sharing only because of the base58 fun fact I didn't know)
ngl, I thought sakana.ai was doing cooler stuff than this. that said, the release of a product like this makes sense because it follows your natural intuition when using these models. The best way to use LLMs is to have at least two in your pocket, because the models do a good job at covering each others assets and filling in obvious pockets of knowledge or coding styles that other models dont have.
it's interesting that they're offering in the form of fixed cost subscription plans too. My impression was that the first party providers can do this because they api inference margins to the tune of 80ish percent. Anyone else orchestrating on top of these models have to pass through these costs or eat it themselves.
Their research around building a domain specific model is pretty cool, it's kind of like Karpathy's autoresearch but pointed at deciding the optimal model to use at each step of the inference.
If cost becomes an even bigger problem being able to choose "best performance possible" or "strong but cost effective" will be useful.
> Frontier-level performance without single-vendor dependency. [...] Plug collective intelligence directly into your workflows today with a single API.
Does multiple vendors run this "single API" or how is this not replacing a single-vendor dependency for another single-vendor dependency?
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.
OpenRouter Fusion is basically ask N models + synthesizer step.
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.
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)?
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!
AI noob question, is this like Amp? I just use Amp, I ask it to do neat stuff and it does it. I desperately need to invest in my AI skills but every day I open two new tabs and add it to "AI stuff" folder, and then go back to drowning in work to do.
While you're at it, feel free to send me $200 as well, I'll generate a crypto address ending with "AI".
(don't send anything, sharing only because of the base58 fun fact I didn't know)
it's interesting that they're offering in the form of fixed cost subscription plans too. My impression was that the first party providers can do this because they api inference margins to the tune of 80ish percent. Anyone else orchestrating on top of these models have to pass through these costs or eat it themselves.
If cost becomes an even bigger problem being able to choose "best performance possible" or "strong but cost effective" will be useful.
https://arxiv.org/pdf/2512.04695
Does multiple vendors run this "single API" or how is this not replacing a single-vendor dependency for another single-vendor dependency?
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.
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)?
We open sourced it all
and will be releasing a similar orchestrator next week on TrustedRouter
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!
https://trustedrouter.com/blog/fusion-evals-open-source