Rendered at 05:37:44 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
holistio 1 hours ago [-]
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".
robertwt7 47 seconds ago [-]
at this point I might just try Neuralwatt and see how much request I can get with GLM5.2. I've read a lot of reviews that its very cheap to run using Neuralwatt cloud
holistio 1 hours ago [-]
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)
someone_1234 27 minutes ago [-]
Or use openrouter and switch to model you want to use..(i think so)
ljlolel 14 minutes ago [-]
Or TrustedRouter if you want privacy and open source
rvz 1 hours ago [-]
Pay $0 to run a local model or even a cheap DeepSeek V4 model via their API which is close to free per million tokens.
These prices are just going to get raced to $0.
holistio 1 hours ago [-]
Maybe. But for now it's fascinating how $200/month has kind of become a normal tier.
It's similar to how AirPods normalised all of us having $300+ headphones. All of us would have scoffed at the idea a decade ago.
p1esk 59 minutes ago [-]
Many people here spent a lot more than $300 on headphones long before AirPods appeared.
mc3301 23 minutes ago [-]
Those were hobbyists, audiophiles, professionals, artists (recording, performing, etc.).
They are talking about a much larger group of people.
holistio 44 minutes ago [-]
I had a really nice Sennheiser before that, too. But now you hop on the subway and everybody sports one.
kijin 59 minutes ago [-]
Not while the hardware required to run a local model at an acceptable speed costs way more than $200.
Guess what, the big players are hoarding all the RAM and GPUs so that other people can't afford decent hardware. It's working out beautifully for them!
audreyt 41 minutes ago [-]
[flagged]
cortesi 1 hours ago [-]
As a developer outside the US I think it's vital to have alternatives to OpenAI and Anthropic, but sadly this is not it. For $200/month you get < 3 hours of use per week, the API is extremely slow, and the output quality in my tests is nowhere near Fable. It's nowhere remotely near usable as a day-to-day workhorse. Very disappointing.
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 model-specific blindspots.
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.
embedding-shape 2 hours ago [-]
> 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?
epsteingpt 50 minutes ago [-]
Beta user: they piloted OpenRouter fusion before it was seen as the viable step. Everyone's understood for months now that having different models check each other is the best path forward.
This gets you that in a nice neat package, without the underlying tinkering mechanics.
If (big iff) the usage mechanics work out, then this is actually a really good anti-big-model strategy.
They'll be incentivized for your success, not token-maximizing for their investors.
The team is super smart too. What's not to like?
Wishing them the best on launch.
prodigycorp 18 minutes ago [-]
if you've used codex or claude, how do the usage limits on fugu feel compared to the pro plans on either? honestly wouldn't mind subscribing to this if it's as generous as what codex is giving me monthly, which seems unrealistic.
epsteingpt 8 minutes ago [-]
Hard to say - since I used it in Beta with free credits, where the usage felt more 'Opus' than 'ChatGPT' but more efficient token wise. Switching models every time is annoying.
But their paid plans I'm not sure yet - planning to subscribe and can let you know.
Almost no chance it will be as generous as OpenAI though. They just don't have the money :-)
GolfPopper 2 hours ago [-]
This is a joke, right?
david_shi 1 hours ago [-]
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.
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.
runeblaze 2 hours ago [-]
links to two papers with at least enough apparent quality and novelty to get into ICLR 2026
> 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)?
adamnemecek 2 hours ago [-]
Seems kinda underwhelming considering they raised like $400M.
ffsm8 57 minutes ago [-]
400m is the new 400k!
Just look at the other company evaluations and how much they raised vs what they delivered
puttycat 2 hours ago [-]
Can someone explain this in layman terms? I don't understand any of it
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.
1 hours ago [-]
bprasanna 2 hours ago [-]
Isn't this what perplexity is?
nickandbro 3 hours ago [-]
Very interesting. I wonder if its kinda functions similarly to how OpenRouter's fusion API does. Hopefully isn't too long to respond.
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!
ljlolel 2 hours ago [-]
I’ve also developed and open-sourced Mythos level model using fusion/synthesis on TrustedRouter
Just letting you guys know that the model is not a moat.
nixosbestos 2 hours ago [-]
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)
These prices are just going to get raced to $0.
It's similar to how AirPods normalised all of us having $300+ headphones. All of us would have scoffed at the idea a decade ago.
They are talking about a much larger group of people.
Guess what, the big players are hoarding all the RAM and GPUs so that other people can't afford decent hardware. It's working out beautifully for them!
https://x.com/cortesi/status/2068898694238486658
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.
Does multiple vendors run this "single API" or how is this not replacing a single-vendor dependency for another single-vendor dependency?
This gets you that in a nice neat package, without the underlying tinkering mechanics.
If (big iff) the usage mechanics work out, then this is actually a really good anti-big-model strategy.
They'll be incentivized for your success, not token-maximizing for their investors.
The team is super smart too. What's not to like?
Wishing them the best on launch.
But their paid plans I'm not sure yet - planning to subscribe and can let you know.
Almost no chance it will be as generous as OpenAI though. They just don't have the money :-)
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
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.
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