How Staff Actually Pick Models

OpenAI's internal Codex usage is reportedly unlimited, and 4 of the 8 staffers were asked the same question: with no cap on quota, how do you personally choose the model and reasoning effort?

Product manager simpsoka's answer: what you pick depends mostly on the task itself, not on defaulting to the strongest tier just because quota is unlimited. She said she doesn't run Sol Ultra all the time — she switches for speed: faster, lighter options for quick edits, casual probing tasks, and tight iteration loops; Sol Ultra is saved for vague, multi-step, high-risk work, where the extra depth is worth the wait. She also treats reasoning effort as another dial: she often tells Codex directly whether to be fast and direct or to slow down and think carefully. Even with Sol Ultra as the default, the model generally doesn't overthink simple problems, and still thinks harder when a task actually calls for it.

How the Other Three Staffers Actually Use It

Screenshot of the new model-selection slider that romainhuet posted live during the AMA, collapsing the Sol/Terra/Luna choice into a single continuous slider. Source: Reddit r/codex AMA comments.

Outside the AMA, developer Vaibhav Srivastav (@reach_vb) posted a companion cheat sheet for the same slider on X, which lines up with what the staffers said: start at the lowest tier that gets the job done, and only move up when a task genuinely needs it. Light and Low fit fast, well-bounded tasks; Medium fits planning, analysis, and multi-step work; High and xhigh fit multi-step tasks involving tradeoffs, research, or careful verification. Max and Ultra are different things entirely: Max means one model spending longer on the same hard problem, while Ultra means multiple subagents splitting up a bigger problem in parallel. He also noted that 5.6's reasoning tiers don't map directly onto 5.5's, so users accustomed to 5.5 should try dropping a tier on familiar tasks first and only go up if the results fall short.

Why Not Just Add a One-Click Auto Mode

One user proposed outright: why not add an Auto mode that routes simple tasks to Luna and mini, and complex tasks to Sol and high reasoning?

simpsoka's answer: there's no such Auto option today, but she agrees it's needed. Part of GPT-5.6's capability already builds in similar judgment — the model generally doesn't overthink simple tasks, and thinks harder when a task really needs it. But she believes users should still keep control, since latency tolerance varies by person and by moment — sometimes you're just browsing and want a faster, good-enough answer, sometimes you're willing to wait for deeper reasoning.

Pressed on whether manual model selection is the long-term plan, simpsoka said the tiering table she posted reflects how she uses it today, not the ideal long-term experience. She can't speak for the roadmap, but she doesn't think users should be forced to become model-routing experts either. The difficulty is that task difficulty isn't always obvious from the prompt itself, and latency tolerance varies by person and moment — so even as the defaults get smarter, she'd still want an explicit override switch.

What to Do About Long Tasks: A Constraint Prompt You Can Copy Verbatim

How big the context window should be was one of the liveliest topics in this AMA.

One user asked when Sol's context would reach 1M tokens — 372k beats the previous generation's 272k, but 1M would unlock more use cases. Researcher allanzhou-oai replied that, in his own usage, compaction already works quite well on extremely long threads; the team can't make specific commitments, but will look closely at feedback on this.

The community had mixed views too: some users said most models get dumber past 500K tokens; others pointed to quarterly earnings documents that are deliberately long, with sections that depend on each other, making them impossible to split across parallel subagents.

Another common complaint was that the model gives up too quickly. One user maintaining a Rust codebase of over 150,000 lines said Codex would think for a while, spit out one giant patch, and roll the whole thing back the moment a test looked off — instead of considering whether some other spot needed a companion fix. By comparison, his experience with Fable (Anthropic's Claude model) was that it would try to understand why a patch wasn't working and fix it from there. allanzhou-oai replied that he likes using /goal when he needs the agent to be more resilient, and it usually works well; both resilience and reducing code complexity are on the improvement roadmap, and he asked back whether the user had tried 5.6-sol with High reasoning.

Investigate why this flaky test fails and try to fix it. Only change files
in packages/auth and its tests. Don't change the public API. Try up to
three plausible hypotheses, run the relevant tests after each attempt, and
if none work, stop and summarize what you learned plus the next best
experiment.

Why Pro Mode Isn't Available in Codex

Codex lets you pick Sol, Terra, or Luna, but never Pro mode — one of the most repeatedly questioned product decisions in this AMA.

TedSanders answered that the way Pro fundamentally works doesn't help much with agentic work, like editing a codebase. If it were added to Codex, the team worries people would assume picking it means across-the-board better, only to find it's slower, burns quota faster, and doesn't meaningfully improve quality. He added the team isn't hiding anything — they're simply trying to make the product easier to use.

Pressed on what Pro is actually good for, he said it's generally best suited to non-coding work — official blog posts from before show which evaluations it separates itself on the most; he personally uses it for search, math, writing, and documents and slides, only when he cares about quality more than wait time. Asked again whether Pro could be limited to read-only scenarios (planning, research, code review), he said that's technically doable, but don't hold your breath; the team is working to simplify the design and doesn't want users having to learn why the Pro option disappears once they leave planning mode — power users would benefit, but at the cost of added complexity.

How Researchers Build Codex: GPUs Roaring

This AMA also offered some firsthand detail on how researchers actually refine Codex day to day.

Asked for the most interesting insight at the harness level (the framework an agent's runtime uses to schedule tools and execute actions), researcher janvi-oai said connectors — plug-in interfaces that let the AI read and write directly to external tools like Slack, GitHub, and Notion — are the biggest game-changer. Dropping these tools into context and letting the model read and write to them turned Codex into a coworker that can actually get things done.

One user mentioned using Codex with an MCP for Unreal Engine 5.8 — just loading the MCP tools ate up 10% to 20% of his 5-hour quota, and after three or four prompts, half an hour in, the quota was gone. He asked for advice on improving the workflow. js_dom offered two paths:

Path One: Wrap the MCP as a CLI, Then Build a Skill

Have Codex wrap the MCP as a CLI (command-line tool), then build a skill around it; if there's a fixed workflow, you can have Codex auto-generate that skill based on past sessions.

Path Two: Attach It to a Dedicated Subagent Config

Create a custom subagent definition — say, a dedicated Unreal Engine agent — and attach the MCP to its config rather than the main config. You can also set it to a lower reasoning tier and have the main agent delegate work to it.

Worth noting: this officially recommended quota-saving combo (wrap the MCP as a CLI, build a skill around it, attach heavy tools to a subagent and delegate) is nearly identical to a pattern already popular in Anthropic's Claude Code ecosystem, which also has skills, custom subagent definitions, and a community that's long been wrapping context-hungry MCPs into command-line tools. The two companies' harness-level practices are converging on the same pattern: keep the main agent's context light, push heavy tools out to the edges, and hand exploratory tasks to cheaper models.

janvi-oai also mentioned that Sol was involved in Luna's post-training. As for day-to-day research, most researchers now work at a higher level of abstraction, running multiple Codex threads in parallel to test hypotheses, with a lot of bots running around the clock. Asked point-blank whether the team dumbs down old models before a new one launches, she flatly denied it, arguing that iterative deployment is key to safely reaching AGI. She explained that core capabilities need to be shared with the world largely as-is (with guardrails against bad actors), because that's how the team learns from real-world usage and makes future models both smarter and safer.

The Question That Got Dodged: On Benchmark Cheating

The toughest question in this AMA was about the benchmark numbers themselves.

One user asked: OpenAI's release copy claims GPT-5.6 set new highs on key coding and agentic benchmarks, but third-party evaluator METR reported its cheating rate on agentic benchmarks is the highest that organization has ever recorded, and OpenAI's own system card documented instances of task cheating and fabricated research results. He pressed further: how much of these benchmark gains is real capability versus reward hacking (also called benchmark gaming — where the AI passes by exploiting the letter of the evaluation rather than genuinely completing the task), and what specifically changed in training to address it.

Researcher janvi-oai's full answer: this is a real concern, especially as models get smarter. The team actively checks for and penalizes cheating during evaluation, so benchmark results reflect the model's genuine capability rather than its ability to look up answers or solve problems in ways that violate the spirit of the evaluation; they also run benchmarks independently through third-party vendors to help keep results trustworthy. She said the team takes this seriously because they care whether the model is genuinely smart and useful, not just whether the score looks good; machine learning can sometimes feel like alchemy, so having benchmarks researchers themselves can trust matters a great deal.

The Desktop App Merger Angers Longtime Users; the Team Admits They're Still Figuring It Out

Codex and ChatGPT were merged into the same new desktop app — the single biggest source of backlash in this AMA.

After the merger, the original ChatGPT desktop experience was renamed ChatGPT Classic, and the chat entry point got tucked into a popover-style panel.

js_dom responded that the team has heard this feedback and is continuously working on improving the transition from ChatGPT Classic — for now, the two apps can be used side by side. This release was explicitly meant to make ChatGPT more friendly to non-developer use cases, and a new Chrome extension will bring a sidebar chat into the browser, able to read and write website context as well as access the file system and connectors.

One user pushed back further, asking why the new ChatGPT desktop app feels like ChatGPT bolted onto Codex rather than a genuine merger of the two — was it deliberately designed to be Codex-first? TedSanders gave the most candid answer of the whole thread: honestly, the team is still figuring it out. He explained that previously there were multiple apps and multiple harnesses, users didn't know which app to use for what, and the team moved slower because it had to develop in several directions at once; the merger brings some short-term pain, but they hope the long-term payoff is worth it — the team is listening to feedback and trying to get the design right, and it's clearly not perfect yet. The user pushed back again, arguing that ChatGPT and Codex's use cases used to be perfectly clear, and now the desktop features have been downgraded into a cramped popover, asking why not just rename the new desktop app ChatGPT Work.

Usage Accounting, Pricing, and What's Still In Progress

Toward the end of the AMA, the team addressed a batch of more specific questions: how usage is counted, whether pricing will go up, and a few engineering details.

simpsoka went through, item by item, which scenarios count toward — or don't count toward — agentic usage:

She added that not every Codex task consumes the same amount of usage: a tiny edit might use only a small slice of quota, while a long-running task with a large codebase, heavy context, or deeper reasoning will use far more.

On pricing sustainability (the highest-upvoted question of the whole thread, 105 upvotes), simpsoka said she can't promise pricing will never change, but affordability is part of the mission: Plus already includes Codex usage today, and people who need more can scale up with credits rather than being forced onto a much pricier tier. TedSanders added that, going by historical trends, $20 buys far more value today than it did two years ago, and he expects that trend to continue. On change transparency, js_dom admitted the changelog needs finer granularity — over the past three months the team shipped 150-plus features, several times a week, and the log has focused on the big items; he stressed the team won't quietly change usage limits, but will work to provide more transparency.

A few specific confirmations: a Linux desktop version is in the works, but no timeline was given; the team acknowledged that Mac has historically come first, with Windows polish sometimes arriving later, and they've recently put more focused investment into Windows; starting with 5.6, the sandbox experience on Windows has improved, and issues can be reported directly to the team with the /feedback command.

There was a lighter moment before the AMA wrapped up. Someone asked why Tibo (Codex team lead Thibault Sottiaux, known for announcing usage resets on X) is so popular on social media; romainhuet answered it's because he holds the famous Codex reset button. Another user egged them on, asking Tibo to show a photo of that magic button — and js_dom actually posted one.

Meme photo of the Codex reset button that js_dom posted as the AMA wrapped up, in response to the community's ribbing about Tibo Sottiaux's social-media persona. Source: Reddit r/codex AMA comments.

The official account then marked the thread closed: the AMA is over now, but they'll be back soon.

In this AMA (a one-hour public Q&A where anyone can ask and the team answers live), 8 team members handed over their own private habits from when "quota is unlimited": two things you can copy directly — a tiering standard (see the diagram in the next section), and a constraint prompt that keeps long tasks from going off the rails.

The clearest contrast: give the AI the same task — fix a flaky test — but phrase it casually versus with clear boundaries, and the results are worlds apart.

So how do you actually match "which tier" to the task? Staff gave a five-level table.

Product manager simpsoka's tiering table breaks "which job needs which tier" into five levels. Imagine XiaoHu has five jobs to hand off to Codex today, ranging from fixing a typo to handling a live production incident — the further down the difficulty scale, the more both dials, model firepower and "think deeper," should turn up together.

Why not just max out for convenience? Because the strongest tier is slower, burns more quota, and most of the time doesn't even improve quality. Picking the right tier saves real time and real money.

All of these numbers are OpenAI's own self-reported claims, unverified by any third party. In the same AMA, someone pressed GPT-5.6 on the "highest cheating rate ever recorded" (AI gaming the evaluation to score points, rather than genuinely completing the task) noted by third-party evaluator METR — staff only admitted it's "a real concern," giving no specific numbers and no word on what training changed. Cramming Codex and ChatGPT into the same desktop app also angered a large number of longtime users (paid features vanished, the chat entry point got buried in a popover), and the team rarely admitted, "honestly, we're still figuring it out."