What is GPT-5.5 Instant used for in MiroFish?

GPT-5.5 Instant is useful as a fast model option for scenario drafting, simulation setup, report iteration, and follow-up questions in MiroFish.

Should every MiroFish run use GPT-5.5 Instant?

No. It is a strong first-pass option for speed and iteration, but high-stakes predictions should still be validated with larger runs, review, or a second model pass.

GPT-5.5 Instant

Use GPT-5.5 Instant when a MiroFish simulation needs fast iteration first.

This guide explains where GPT-5.5 Instant fits inside MiroFish: quick scenario setup, responsive multi-agent runs, report drafting, and follow-up exploration before you spend time on deeper validation.

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Quick answer

GPT-5.5 Instant is a practical first-pass model choice for MiroFish when the value comes from running more iterations, testing several scenario framings, and getting a report draft quickly enough to keep exploring.

For MiroFish users, that means it is especially useful early in the workflow: turning a question into seed context, refining agent assumptions, checking whether the simulated world is coherent, and asking follow-up questions after the first report.

How to use GPT-5.5 Instant in a MiroFish workflow

Start small

Begin with a clear question, a compact seed, and a small simulation. Use the first output to check whether the scenario framing is pointed in the right direction.

Inspect the report

Look for missing actors, unrealistic incentives, or weak assumptions. Fast output is most valuable when it helps you revise the next run sooner.

Scale after signal

Increase agent count, rounds, or seed detail after the first useful signal appears. This keeps cost and review time tied to actual learning.

When to choose it

You want several scenario drafts before committing to one framing.
You need fast follow-up questions on a generated prediction report.
You are comparing public narratives, personas, reactions, or market sentiment paths.
You want a cloud model default before configuring a custom self-hosted provider.

When to slow down

The decision is high stakes and needs expert review.
The seed material is long, conflicting, or highly technical.
The first-pass report reveals assumptions that need a deeper second run.
You need to compare model behavior across providers for governance reasons.

Evaluation checklist

Does the model preserve the scenario premise across graph building, simulation, and report generation?
Can you explain why the simulated agents reacted the way they did?
Did the report reveal a decision-relevant risk, narrative, or turning point?
Would a second run with more agents or a deeper model change the decision?

A useful rule: use GPT-5.5 Instant to find the shape of the problem quickly, then reserve deeper validation for the scenarios that still matter after the first report.