Fugu Ultra v2

1. Multi-model routing

This is the real product.

Fugu decides whether a task needs to be split up, which model should handle each part, and how the pieces should be stitched together afterward.

If you already build agent workflows or model routers yourself, that could remove a fair amount of plumbing.

I would not use it just because it can route across several models. Routing only matters when the task is complicated enough to benefit from it.

A short translation or quick summary probably does not need an orchestration layer. Research, codebase analysis, and longer development jobs are where I would expect it to earn its keep.

2. A 1 million token context window

The 1 million token limit sounds impressive. I care more about where the pricing changes.

Once the prompt goes past 272K tokens, the API gets more expensive.

So I would treat the million-token window as capacity, not a target. If a codebase has 600K tokens, I still would not automatically dump all 600K into every request.

Filtering first is usually the more sensible move.

The same applies to research. Having room for a huge pile of documents is useful. Paying to resend all of them every time is another question.

Fugu also supports image input and web search, which makes the large context window more useful for research-heavy jobs.

3. OpenAI-compatible API

Fugu uses an OpenAI-compatible API, which is good news for developers who already have that kind of integration in place.

You do not need a completely new client just to try it.

What I would watch is how much control gets hidden behind that convenience.

Some teams will be happy to let Fugu decide which model runs each step. Others will want tighter control over routing, cost, and model choice.

If you fall into the second group, building the workflow yourself may still be worth the extra work.

4. The biggest change from v1 is the model pool

Ultra v2 drops Fable 5, Fable 5.1, and GPT-6 Astra. Its training cutoff is now August 28, 2026.

I find that more interesting than another benchmark bump.

A routing system is much more useful if the models underneath it can actually be swapped out. If one provider changes pricing or an API, the whole product should not have to move with it.

That is the direction v2 is pushing toward.

For developers coming from v1, the migration is small. The SDK stays the same. The model ID becomes:

fugu-ultra-v2.0

Fugu Ultra v2 looks most useful when the job is too big or too messy for a single clean prompt.

Its main advantage is not the 1 million token window or the benchmark score. It is the fact that task splitting and model selection happen for you.

That is useful if you work on research, large codebases, agent workflows, or anything else with several dependent steps.

The weak spots are just as practical: internal routing can drive up token usage, long-context pricing climbs after 272K tokens, and you give up some control over what happens inside the run.

I would recommend testing it if you regularly deal with long, multi-stage tasks or do not want to maintain your own routing layer.

I would skip it for everyday writing, translation, quick questions, or small coding jobs. A single good model is simpler there.

Personally, I would use Fugu Ultra v2 for codebase analysis and long research jobs first.

If it saves me work, I keep it.

If I end up cleaning up the router’s decisions afterward, I do not.