ChatGPT 5.6 Sol | Sol Vs Fable 5 | ChatGPT Work Features

6 Best Use Cases of OpenAI’s New ChatGPT 5.6

Computer use, ultra mode, subagents, and pricings etc

ChatGPT 5.6 (Image by samaatv)

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OpenAI released GPT 5.6 on July 9, after a limited preview that started June 26 because the US government asked to look at its cyber capabilities first.

The family comes in three tiers. Sol is the flagship at $5 per million input tokens and $30 output, Terra sits at $2.50 and $15, and Luna at $1 and $6, which makes the whole lineup cheaper than Anthropic’s comparable models.

Sol ships with a 1.05 million token context window, 128K max output, and the model ID gpt-5.6-sol in the API.

On paper it’s a coding model. OpenAI claims a new state of the art of 80 on the Coding Agent Index, 2.8 points above Claude Fable 5, while using less than half the output tokens and less than half the time.

But the specs only matter if they translate into things you can actually do differently.

Here are the six that do.

1. Builds that check themselves before you see them

The headline feature here is Sol’s upgraded computer use.

It doesn’t just generate code. It opens the rendered result, inspects it visually, catches a broken layout or a dead button, and applies fixes before handing anything back, which is a review step older models simply skipped.

OpenAI is also claiming a step change in design judgment, meaning Sol produces tasteful, functional interfaces from only high level direction, territory that until this release belonged almost entirely to Claude.

The same loop covers security. Because Sol reads and reasons about code at a frontier level, developers are already running it as a pre ship auditor for threat modeling and code review, a workflow OpenAI explicitly lists as supported defensive use.

Example: Ask for a checkout page, and Sol builds it, renders it, notices the pay button breaks on mobile, fixes the spacing, then delivers, all in one shot.

2. Long horizon work through max and ultra modes

Sol introduces two new reasoning controls, and they’re the real story behind the “runs for days” claims.

• max gives a single agent the most time to reason deeply on one hard problem.
• ultra goes beyond a single agent entirely, coordinating multiple parallel subagents on the same assignment, and it’s available to Pro and Enterprise users in ChatGPT Work.

This is what state of the art on Terminal Bench 2.1 looks like in practice, a benchmark built around command line workflows that demand planning, iteration, and tool coordination, exactly the skills that keep a model on task for hours.

The subagent behavior also shows up unprompted. In research settings Sol has spawned its own subagents to parallelize exploration, without being told to, which is the closest thing to genuine delegation any public model has shown.

One caveat: a model that works unattended needs boundaries, so keep anything destructive behind explicit permissions.

3. Frontier cybersecurity, with a gate on it

Sol is OpenAI’s strongest cybersecurity model yet, and this is the capability that delayed the launch.

On ExploitBench, which measures building progressively more capable V8 exploits, Sol is competitive with Anthropic’s restricted Mythos tier while using roughly a third of the output tokens.

That’s why the most sensitive capabilities sit behind a Trusted Access for Cyber program reserved for vetted organizations, and why OpenAI says its new safeguards block roughly ten times more harmful activity than before, after around 700,000 GPU hours of automated red teaming.

For the rest of us, the defensive side is open: threat modeling, code review and patching, and blue teaming your own systems, meaning simulated attacks on your own stack to find the weaknesses before someone else does.

4. Messy context into finished deliverables through ChatGPT Work

This launch wasn’t just a model. OpenAI merged Codex into a rebuilt ChatGPT desktop app, with a built in browser and computer control, and shipped ChatGPT Work on top of it, its answer to Anthropic’s Claude Cowork.

Work pulls context from Slack, Notion, Google Drive, and Microsoft 365, then turns it into shareable slides, spreadsheets, and documents that follow your brand guidelines.

The differentiator isn’t raw intelligence. It’s that Sol follows reference formats faithfully instead of improvising, backed by the same frontend ability that turns natural language requests into interactive visualizations inside Work.

Internally, OpenAI says nearly all of its own teams now run on it, with finance teams cutting month end close from days to hours, which is about as direct a preview of the target customer as you’ll get.

5. Science and research workloads, not just code

The benchmark gains that got less attention are in biology and scientific research.

On GeneBench Pro, a long horizon genomics and quantitative biology benchmark, Sol reaches stronger results with fewer tokens and less time than GPT 5.5, and OpenAI reports Pareto improvements across real world life science workflows and chemistry.

Like cyber, the most sensitive end of this sits behind a Trusted Access for Biology Research program.

The pattern for everyone else is “explore this space computationally while I think.” Feed it a data heavy problem, let it parallelize with subagents, and review what held up, work that would have taken weeks by hand.

Example: A mathematician used it to run computational explorations on a three year old conjecture and helped settle it, which is less about math and more about what persistent, parallel compute does for any open question.

6. Economics that change what you bother to automate

Sam Altman’s number is that Sol is 54% more token efficient on agentic coding than the previous generation, and the pricing structure compounds it.

• Cached input drops to $0.50 per million tokens, a 90% discount.
• GPT 5.6 adds explicit cache breakpoints and a 30 minute minimum cache life, so long running agent sessions stop paying full price to re read their own context.
• Terra performs just above Fable 5 at half of Sol’s price, and Luna outperforms Opus 4.8 at a fraction of it, per OpenAI’s own comparisons.

Two workflows fall out of this.

First, the verification layer. Fable plans and designs, Sol builds and fact checks, and because Sol runs at about a third of the cost per task, a full second pass over expensive output costs almost nothing next to the mistakes it catches.

Second, the pile of dull work you never automated because it wasn’t worth it: recurring forms, spreadsheet cleanup, statement analysis, data entry. None of it needs frontier intelligence. It needs a model cheap enough that you stop rationing it, and that’s the actual product here.

So does it replace Fable 5?

On the Artificial Analysis Intelligence Index the two are nearly tied, 59 against 59.9, with Sol at roughly a third of the cost per task.

But the launch week consensus holds. Fable still wins on design taste, deep analysis, and the hardest open ended work, the stuff you have to push yourself to even consider possible.

Sol is the daily driver. The early tester line that stuck with me: it handles about 90 percent of what Fable can do, and it does it without making you think about the bill.

Use Fable when the decision is make or break.

Point Sol at everything else.