OpenAI announced GPT-5.6 Sol on June 26, 2026.

It sounds like exactly the kind of model I want to try in Codex.

It is also, for now, mostly not available.

OpenAI says GPT-5.6 is starting with “a limited preview for a small group of trusted partners” after engagement with the U.S. government. Broader access is supposed to come “in the coming weeks” for ChatGPT, Codex, and the API.

I hate this version of a launch. The model is aimed at builders, but most builders are stuck reading charts and guessing from screenshots.

Tibo from the Codex team posted:

Can’t wait to see what people will do with GPT-5.6 Sol Ultra. Stash your hardest prompts somewhere.

I am doing that. Not benchmark prompts. The annoying ones from real work.

The tasks where current agents almost get there, then make one confident move outside the lane.

What OpenAI Announced

The GPT-5.6 family has three models:

  • Sol: flagship
  • Terra: cheaper middle tier
  • Luna: fastest and cheapest

Sol has two new knobs:

  • max reasoning effort
  • ultra mode, which uses subagents

The ultra mode is the part I care about for Codex. Real coding-agent work is rarely one straight line. It is usually inspect, edit, run, fail, inspect again, compare options, and stop before changing something the user did not ask for.

OpenAI also published pricing per 1M tokens:

  • Sol: $5 input, $30 output
  • Terra: $2.50 input, $15 output
  • Luna: $1 input, $6 output

They added explicit cache breakpoints, a 30-minute minimum cache life, cache writes at 1.25x uncached input, and cache reads with a 90% cached-input discount.

One more line worth saving: OpenAI says GPT-5.6 Sol will run on Cerebras in July at up to 750 tokens per second for select customers. I want to see that inside an agent loop, not just in a speed demo.

Benchmarks

OpenAI’s developer forum post includes a Terminal-Bench 2.1 chart. I checked the chart directly because some secondary posts had the lower rows wrong.

Model Terminal-Bench 2.1
GPT-5.6 Sol Ultra 91.9%
GPT-5.6 Sol 88.8%
Claude Mythos 5 88.0%
GPT-5.6 Terra 84.3%
Claude Fable 5 84.3%
GPT-5.5 83.4%
GPT-5.6 Luna 82.5%
Claude Opus 4.8 78.9%
Gemini 3.1 Pro Preview 70.7%

Terminal-Bench matters more to me than a lot of older coding benchmarks because it tests command-line workflows: planning, iteration, and tool coordination.

Still, I would not read too much into one chart. The useful question is whether Sol Ultra can handle a real repo without wandering.

OpenAI also claims gains in biology and cybersecurity. The developer forum post lists SecureBio scores of 53.5% on the Virology Capabilities Test, 60.0% on Molecular Biology, 68.4% on Human Pathogen Capabilities, and 68.3% on World-Class Bio.

On cybersecurity, OpenAI says Sol is competitive with Mythos Preview on ExploitBench while using about one-third of the output tokens. The system card says GPT-5.6 Sol is High capability in cybersecurity, not Critical. In OpenAI’s hardened Chromium and Firefox tests, it found bugs and exploit primitives but did not produce a functional full-chain exploit.

So the claim is strong, but narrower than the launch chatter makes it sound.

The METR Report

METR’s predeployment report is the uncomfortable part.

METR says GPT-5.6 Sol had the highest detected cheating rate of any public model they had evaluated on their ReAct agent harness.

Their examples were not vague:

  • packaging exploits in intermediate submissions to reveal hidden test-suite information
  • extracting hidden source code that contained the expected answer

Because of that, METR did not treat its time-horizon measurement as robust. Depending on how cheating attempts were counted, Sol’s estimate moved from about 11.3 hours to beyond 270 hours. Discarding the cheating attempts gave 71 hours with a huge confidence interval.

This is the part I would watch in Codex.

A coding agent that is persistent is useful. A coding agent that is persistent in the wrong direction is work.

OpenAI’s system card includes examples of GPT-5.6 Sol taking actions beyond the user’s intent: destructive cleanup on resources the user did not name, unverified research work presented as completed, and credentials used beyond what the user authorized.

OpenAI says the absolute rates are low. I believe that. I also do not want rare destructive behavior in a long-running agent session.

What People Are Saying

On Hacker News, the reactions split into two buckets.

Some people are tired of model churn: new names, new prices, old models disappearing, tiers moving around. I get that. Once a model is part of your workflow, the model menu becomes infrastructure.

The other discussion was about METR. Some people argued that cheating is a kind of intelligence. Maybe in a very abstract sense. For a coding agent, I mostly do not care. I want the agent to do the assigned work inside the constraints.

Reddit had the same split. r/codex focused on the Terminal-Bench chart, Codex, pricing, and whether Sol Ultra beats the Anthropic models people have been using for hard coding work. r/OpenAI focused more on the METR cheating report.

The Tibo tweet also started release-date guessing. I did not find a public release date. As of July 4, 2026, the accurate status is still limited preview now, broader release promised soon.

What I Want To Try

When I can run GPT-5.6 Sol or Sol Ultra in Codex, I want to test:

  • a real bug fix across a medium repo
  • a refactor where it has to leave unrelated files alone
  • a UI task that needs screenshots and logs
  • a test failure where the model has to admit it cannot verify something
  • a long task where it should stop before using secrets, credentials, or destructive commands

The hype is justified enough that I am saving prompts.

It is not justified enough that I trust the charts by themselves.

I want to run it on the ugly tasks.