Fugu Max

1. Cost-first model routing

This is the biggest difference between Fugu Max and Ultra v2.

Ultra v2 generally leans toward stronger models. Fugu Max tries to find the lowest-cost option that can still complete the task.

For example, a batch of document summaries, basic code generation, or structured data processing may not need the strongest model every time.

That is where Max makes sense.

The trade-off appears on harder jobs.

If the task involves architecture decisions, deep reasoning, or anything where mistakes are expensive, a cheaper model may not be enough.

If I were testing Fugu Max, I would not start with important projects. I would run a batch of lower-risk tasks first and see whether the time saved is actually worth the quality difference.

The model pool is also part of this equation. Ultra v2 and Max are not simply “better” and “cheaper” versions of the same product. They make different choices.

Ultra v2 gives more weight to the final answer.

Max gives more weight to the cost.

2. Larger model pool, including NVIDIA Nemotron

Fugu Max uses one of the larger model pools in the Fugu lineup.

Sakana has specifically mentioned adding NVIDIA’s Nemotron family through its partnership with NVIDIA.

A larger pool gives the routing system more options when choosing a model.

The limitation is visibility.

Users cannot see exactly which model handled each request.

Sakana does not provide full routing logs, so developers cannot directly check:

• which models were used;
• why a certain model was selected;
• how much each step cost.

For casual use, that may not matter.

For teams trying to control spending or improve workflows, it can make troubleshooting harder.

3. OpenAI-compatible API

Fugu Max uses the same OpenAI-compatible API style as Ultra v2.

Existing Fugu users do not need to rebuild their setup. Switching only requires changing the model ID:

fugu-max

That makes it easy to compare Max and Ultra v2 inside the same application.

The downside is the same: internal routing remains hidden.

If you need to force a specific model at every step or keep exact control over costs, a custom workflow may still be the better choice.

Fugu Max is mainly about making multi-model AI cheaper to run.

The lower API price is the reason to look at it. For document processing, repetitive analysis, basic coding tasks, and other workflows where the answer does not need to be perfect every time, the cost difference can matter.

I would test it on low-risk, high-volume work first.

If it saves enough time while keeping quality acceptable, I would keep it in the workflow.

If I spend more time checking and fixing the output than I save on API costs, I would drop it.

For architecture decisions, difficult reasoning tasks, or anything where one wrong answer is expensive, I would still use Ultra v2.

I would not replace Ultra v2 with Fugu Max just because it is cheaper. I would only make that switch if it proves that the lower cost also saves me time.