GPT-6.1 Sol Review: My First Day Using It in Codex

A Better Working Habit

The first behavioural difference I noticed was simple: GPT-6.1 Sol asks more questions. When a request leaves room for interpretation, it is more willing to check what I mean instead of confidently choosing a direction and making me discover the assumption after the work is done.

That can look slower if the only metric is how quickly code appears. In practice, one useful question is often cheaper than reviewing a polished implementation built on the wrong idea. It is especially welcome in frontend work, where words such as clean, compact, prominent, or consistent can mean very different things across a site.

I do not want a model to ask permission for every harmless edit. So far, GPT-6.1 Sol has felt closer to the useful balance: clarify the decision that changes the outcome, then get on with the work. I would rather answer a precise question at the beginning than unwind an unnecessary redesign at the end.

Cost and Codex Usage

The second difference is cost. In my Codex usage, the allowance meter has been moving much more slowly than it did with Astra. It has also been slower than my recent GPT-5.6 Sol High sessions. That is a personal observation rather than a controlled price comparison: the tasks, context, tool calls, and reasoning effort are never perfectly identical.

The official API prices point in the same direction. For prompts up to 272,000 input tokens, OpenAI lists GPT-6.1 Sol at $2 per million input tokens and $10 per million output tokens. GPT-6 Astra is listed at $10 and $50 respectively. The earlier GPT-5.6 Sol launch listed $5 input and $30 output. Codex plan limits do not map cleanly onto those API prices, but cheaper inference gives OpenAI more room to make useful work affordable.

What matters to me is not the cheapest token. It is how much finished work I get before usage becomes the constraint. On that measure, GPT-6.1 Sol has made a strong first impression.

Actual Frontend Work

I have been giving GPT-6.1 Sol relatively short prompts for frontend work that still touches several pages. It has been able to trace the relevant components, carry a design decision across those pages, manage the edits, and stay with the task through verification. I have not needed to describe every file or prescribe every individual change.

That matters more than a clever answer in chat. Codex is useful to me when the model can work inside the repository, respect the existing design, make related changes together, and return something I can review rather than a new planning conversation. GPT-6.1 Sol has been doing that well.

A multi-page change is a good practical test because consistency is part of the task. Updating one hero is easy. Finding the shared pattern, changing the right pages, leaving unrelated pages alone, and checking the complete result requires the model to hold the job together for longer. My early tasks have reached that bar without an exhaustive prompt.

Where It Fits in My Workflow

I will still supervise it. A model can misunderstand a visual preference, preserve a pattern that should have been questioned, or pass automated checks while missing the point of the page. My workflow has not changed: define the outcome, let Codex execute, inspect the result, and run the checks. The difference is that the execution has felt capable without consuming Astra-sized usage.

I also do not need one model to win every comparison. Astra remains available for work where the strongest possible reasoning earns the extra usage. GPT-5.6 Sol High gave me another capable option. GPT-6.1 Sol is interesting because it currently covers a large part of my ordinary implementation work while leaving more of the allowance available for the next task.

My Early Verdict

The appeal is the combination: useful clarification, lower observed usage, and dependable execution. Any one of those would be welcome. Together, they make GPT-6.1 Sol easy to select for the next real piece of work instead of saving it for a demonstration.

I expect the limits will become clearer as I give it larger architectural tasks and less familiar repositories. For now, I have enough evidence for a narrow conclusion: on the frontend work I actually do, it has been capable, economical, and pleasant to direct.