Why New AI Models Feel Worse At First

In the first few seconds after launch, a model can seem slow and overly cautious.
That first impression can shape its reputation.
A different picture can appear during repetitive work.
Examples include fixing code, running tests, and reading PRs.

TL;DR

Some new models feel slower at first because reasoning time, caution, and confirmation behavior can differ.
This matters because official documents describe adjustable thinking time, effort levels, and workflow-focused coding use cases.
Re-test the same task in chat and in a repeated workflow before making an adoption decision.

Example: A team tries a new model in chat, feels friction, and plans to reject it. Later, they compare it inside a coding workflow and notice a different tradeoff.

Current situation

The official documents provide a hint.
The OpenAI Help Center release notes dated Jan 10, 2026 mention periodic adjustments.
They describe default thinking time for reasoning models.
They frame this as balancing answer quality and response speed.
That suggests early slowness may not be a fixed trait.

Model design also affects first impressions.
OpenAI release notes say some reasoning-series models are designed to think longer before answering.
The same model can therefore feel different across settings.
Speed, answer length, and cautiousness can all vary.

Confirmation procedures are another variable.
The Model Spec highlights chain of command.
It also highlights “Seek the truth together.”
It also highlights harmfulness boundaries.
These principles can increase assumption checks and uncertainty checks.
Users may experience that as extra confirmation steps.

The coding tool documentation is more workflow-oriented.
The Codex materials discuss PR review and multiple files.
They also discuss terminal use and SSH access to a remote devbox.
They include testing apps and running test harnesses, linters, and type checkers.
In that setting, sustained context across steps can matter more than the first reply.

Analysis

Early evaluation has a clear trap.
In chat, people often reward immediate replies.
Models that reason longer can look worse at first.
The same can happen with models that ask confirmation questions.
Tighter safety behavior can also affect initial perception.

The official documents describe several moving parts.
Thinking time can be adjusted periodically.
Reasoning effort can vary across 3 levels.
Production behavior can also vary with system updates and prompts.
That makes launch-day judgment narrower than it first appears.

This does not mean long-term use fixes everything.
The official materials do not show a systematic reversal in reputation.
They mainly describe release-time benchmarks, safety evaluations, response length, and latency.
They also describe operational variation.
That is useful context, but not direct proof of later improvement.

There is still uncertainty.
Repetitive work may reveal strengths that chat does not show.
It may also reveal friction from frequent confirmations.
That friction can affect both speed and flow.
For short question-and-answer use, that drawback may remain important.

Practical application

The evaluation method should change.
Ask how many round trips a task required.
Do not group chat, coding, and review into one score.
Measure them separately.
Compare a short Q&A with a flow that includes code edits and test execution.

Development teams should evaluate models inside real tools.
That can mean an IDE or an agent tool.
The official scenarios are closer to multi-file editing than one-off answers.
They also include terminal execution, PR review, and remote environments.
Useful metrics include retries, round trips to passing tests, and review misses.

Checklist for Today:

Split one task into a single-question version and a repeated workflow version, then record each result separately.
If reasoning effort is configurable, compare at least 2 settings: one speed-oriented and one quality-oriented.
Re-check any model that asks for confirmation during cumulative tasks like PR review, testing, and refactoring.

FAQ

Q. Should launch-day evaluations not be trusted?
A. It is better to narrow their scope.
They can reflect first impressions and short interactions.
They do not stand in for repetitive work performance.

Q. Do the official documents prove that the model becomes better after long-term use?
A. No.
The documents confirm adjustable speed, reasoning, and behavioral principles.
They also note possible operational variation.
They do not provide quantitative proof of a long-term reversal in reputation.

Q. For which users does this issue appear most strongly?
A. Short Q&A users and development workflow users may feel it differently.
The first group may notice latency and confirmation questions more.
The second group may reach a different judgment during tests and PR review.

Conclusion

A new model often earns its first reputation in the chat window.
Its adoption value may depend more on repetitive work.
Speed, reasoning time, and confirmation behavior should be treated as variables.
They should be re-evaluated in the actual workflow.

Aionda