GPT-5.5 Workflow Completion Map

What This Proves

The original plan was a full GPT-5.5 versus GPT-5.4 shootout: five prompts, two terminals, screen recordings, browser control, Sheets, Slides, PDFs, dictation, and auto-review mode. That would have been visually strong, but it was too much ceremony for the actual question I needed answered today.

The useful question was smaller:

Where does GPT-5.5 change the workflow decision?

This build turns the release into a routing map. If the job is a one-line copy edit, do not burn the expensive model. If the job crosses code, browser, docs, screenshots, tests, and review, GPT-5.5 is the model to try because the release is aimed at finishing more of the loop with fewer supervision turns.

What I Built

The interactive scorecard above groups the launch into four workflow lenses:

1. Agentic coding - longer code tasks with tools and tests.
2. Computer work - browser, files, spreadsheets, docs, PDFs, and screenshots.
3. Research loops - source gathering, comparison, and usable briefs.
4. Review and QA - code review and issue-finding workflows.

Each lens has three evidence cards, a "use it when" rule, a guardrail, and a verdict. The point is not to crown GPT-5.5 as the best model for everything. The point is to know where the upgrade changes the operating loop.

Why This Approach Worked

Launch posts usually collapse into one of two weak shapes:

• a benchmark table with no workflow translation
• a hype take with no evidence

This build sits between them. It uses official evals from OpenAI's GPT-5.5 release post, the GPT-5.4 release post, and early workflow reports like CodeRabbit's review benchmark. But the output is not "look at the numbers." It is "here is how I would route work on Monday."

That matters because GPT-5.5 is priced like a premium work model. OpenAI's Apr 24 update says gpt-5.5 and gpt-5.5-pro are now available in the API. The standard API price listed in the release post is $5 per million input tokens and $30 per million output tokens for gpt-5.5, with Fast mode in Codex generating tokens 1.5x faster for 2.5x the cost.

The cost is not a footnote. It is the whole routing problem.

Patterns Worth Borrowing

• Translate evals into jobs. Terminal-Bench matters for agentic terminal work. OSWorld matters for computer use. BrowseComp matters for research loops. Do not use one score as a universal model ranking.
• Route by supervision cost. The premium model is worth it when failed intermediate steps cost more than tokens.
• Keep the old default for low-stakes work. A stronger model can still be the wrong default for short, obvious, low-value tasks.
• Write the guardrail next to the claim. If a release says "computer work," the paired guardrail is scope, dry runs, and human confirmation for risky actions.
• Ship the map before the giant benchmark. A small, honest routing artifact is more useful than a perfect test that never publishes.

Limits Or Caveats

I did not run the original five-prompt shootout yet. This is a release-read build, not a lab benchmark.

The official evals are real signals, but they are not your workflow. OpenAI also notes evidence of memorization risk on SWE-Bench Pro, which is exactly why I do not use that number as the headline proof.

Community reports are early and uneven. Some users feel the quality jump immediately. Others feel the cost and quota pressure first. Both can be true.

What I Would Test Next

The next useful test is not "which model is smarter?" It is:

Given the same messy workflow, how many supervision turns does each model leave behind?

That is why the next artifact is a concrete inbound-lead replay: same messy packet, same requested proposal pack, and a visible score for the amount of business work still left for the human.

Related workflow: Demo GPT-5.5 with an inbound lead to proposal pack.