GPT-5.5 Review: I Built 4 Apps and Here's What I Found

OpenAI released GPT-5.5 on April 23, 2026 — just one week after Anthropic launched Claude Opus 4.7. The timing is no accident. GPT-5.5 beats Opus 4.7 on most standard benchmarks, scores 82.7% on Terminal-Bench 2.0 against Opus's 69.4%, and OpenAI president Greg Brockman called it "a new class of intelligence." But benchmarks only tell half the story. In this review, I combine the official numbers with a hands-on test building four real apps via Codex and the results are more mixed than the launch page suggests.

Key Takeaways

• GPT-5.5 scores 82.7% on Terminal-Bench 2.0 and 84.9% on GDPval, outperforming Claude Opus 4.7 on most agentic benchmarks. (Source: OpenAI)

• Claude Opus 4.7 retains its lead on SWE-Bench Pro (64.3% vs GPT-5.5's 58.6%), meaning Opus still handles complex real-world GitHub issues better. (Source: The New Stack)

• In hands-on Codex testing across four app builds — full-stack, frontend UI, Mac app, and a game — GPT-5.5 scored 5/5 on three but only 2.5/5 on the full-stack app.

• The model matches GPT-5.4 per-token latency while using fewer tokens, and OpenAI claims it delivers frontier coding at half the cost of competing models. (Source: Interesting Engineering)

• API pricing is $5 per million input tokens and $30 per million output tokens — twice GPT-5.4's rate. (Source: The New Stack)

• GPT-5.5 Pro is limited to Pro, Business, and Enterprise ChatGPT users. The API will follow after additional safety requirements.

What Is GPT-5.5?

GPT-5.5 is OpenAI's latest frontier model, designed around one core idea: agentic computing. Rather than waiting for a user to guide every step, GPT-5.5 takes a broader task, breaks it down, uses tools, reviews its own intermediate results, and pushes through to completion.

OpenAI identifies four areas of major improvement: agentic coding, computer use, knowledge work, and early scientific research. The model runs inside ChatGPT and Codex, with API access coming soon.

GPT-5.5 comes in three variants:

Codex users get a 400,000-token context window with GPT-5.5, plus an optional Fast mode that runs 1.5x faster at 2.5x the token cost.

GPT-5.5 Benchmark Scores vs. Competitors

OpenAI published benchmark comparisons against Claude Opus 4.7 and Gemini 3.1 Pro. The numbers show clear wins for GPT-5.5 in agentic and math tasks, but gaps remain in reasoning and code-review benchmarks. (Source: Interesting Engineering)

Independent benchmark platforms like CodeRabbit ran their own tests. On a real-world code review set, GPT-5.5 raised the expected issue found rate from 55.0% to 65.0% and improved precision from 11.6% to 13.2%. (Source: CodeRabbit)

Hands-On: Building 4 Apps with GPT-5.5 in Codex

This is where the review gets practical. Using Codex with GPT-5.5 in high-thinking mode (not extra-high, to manage rate limits), four apps were built from a single prompt each. No iteration. One go.

App 1: Full-Stack Web App — Staff Scheduling Platform

Score: 2.5 / 5

The task was a restaurant and retail staff scheduling app — ShiftBoard — with dual login (manager and employee), shift creation, approval flows, and a notification system.

The first version had major issues. The notification "mark all as read" didn't work. Settings and employee list pages opened without navigation. Adding and saving shifts failed. Approval and deny buttons did nothing. The UI logic was broken across most core flows.

After Codex used its browser-use feature — where it visually browsed the live app, identified that the database was running in a no-op demo mode, and auto-fixed the issue — functionality improved significantly. Shifts could be saved, leave requests worked, and the employee availability flow functioned correctly.

But navigation remained poor. The overall UX felt rough. Compared to Kimi K 2.6, Minimax 2.7, GLM 5.1, and Claude Opus 4.7 — all of which handled the same full-stack app without iteration — GPT-5.5 was the weakest of the group.

The browser-use self-correction in Codex is a genuine differentiator and worth noting. But it shouldn't be needed on the first pass for a frontier model.

App 2: SaaS Analytics Landing Page

Score: 5 / 5

This one was impressive. The prompt called for a detailed SaaS analytics landing page with specific colors, typography, spacing, cursor animations, hover effects, and animated SVG mockups.

GPT-5.5 delivered everything in one go. Cursor animations fired on the correct dashboard elements. Hover effects on feature cards matched the spec. The pricing section switched between monthly and annual correctly. All visual elements — including the mockup panels — were coded in SVG rather than pulled from external images.

For UI/UX frontend work, GPT-5.5 clearly excels.

App 3: Mac Font Manager App

Score: 5 / 5

The prompt asked for an Electron-based Mac app that scans system fonts, shows live previews, allows tagging, favoriting, comparison mode, notes, and export as PNG.

The result looked native to macOS. Grid mode and comparison mode both worked. Keyboard shortcuts — including Cmd+F for search and arrow key font navigation — all functioned correctly. The font name copy-to-clipboard worked. Export as PNG saved correctly to the downloads folder. Tags and notes persisted.

Only the font size slider didn't work. Everything else: clean execution.

App 4: Gem Blast Game (Candy Crush Clone)

Score: 5 / 5

A match-3 puzzle game with levels, scoring, graphics, animations, and sound effects. GPT-5.5 built it in one prompt. The game launched correctly, level progression worked, points tracked, and the sound system fired on match events.

Visually it was polished and playable. For a game built in a single pass, this was a standout result.

Overall Score Summary

Pricing: What GPT-5.5 Actually Costs

GPT-5.5 API pricing doubled compared to GPT-5.4. (Source: The New Stack)

OpenAI argues the higher cost is offset by token efficiency — GPT-5.5 completes the same Codex tasks with fewer tokens than GPT-5.4. The company claims GPT-5.5 delivers state-of-the-art intelligence at half the cost of competitive frontier coding models. (Source: Interesting Engineering)

What GPT-5.5 Is Best For

GPT-5.5 performs at its highest level for:

Frontend and UI/UX work. The SaaS landing page result shows the model handles design-spec fidelity well. If the prompt includes precise layout, animation, and component requirements, GPT-5.5 delivers.

Desktop and native-style apps. The Electron font manager came out looking Mac-native in one shot. This is harder than it looks.

Games and creative coding. Match-3 logic, animation triggers, sound system integration — GPT-5.5 handles multi-system interactive builds without needing hand-holding.

Knowledge work agents. With 84.9% on GDPval across 44 occupations and 98.0% on Tau2-bench Telecom customer service workflows, GPT-5.5 clearly targets enterprise task automation, not just code generation. (Source: OpenAI)

Scientific research assistance. GPT-5.5 set leading performance on BixBench (bioinformatics) and GeneBench (genetics, multi-stage data analysis). OpenAI described the results as capable of accelerating biomedical research progress as a genuine co-scientist. (Source: OpenAI)

Where GPT-5.5 Still Falls Behind

Full-stack app reliability. Kimi K 2.6 built the same shift-scheduling app without any issues. So did Minimax 2.7, GLM 5.1, and Claude Opus 4.7. GPT-5.5 needed a browser-use debugging pass to get core features working. For a frontier model with a high-thinking mode, that gap matters.

SWE-Bench Pro. Claude Opus 4.7 scores 64.3% against GPT-5.5's 58.6% on this benchmark, which tests real-world GitHub issue resolution. For developers who use AI for code review and bug-fixing in production codebases, Opus still has the edge. (Source: The New Stack)

Multi-disciplinary academic reasoning. Gemini 3.1 Pro leads on ARC-AGI-1 and BrowseComp. Claude Opus retains an edge on MCP Atlas and Humanity's Last Exam. GPT-5.5's benchmark dominance is real but uneven. (Source: Interesting Engineering)

The Codex Browser-Use Feature

One capability stands out beyond the model itself: Codex's browser-use mode. After GPT-5.5 built the broken ShiftBoard app, Codex opened a live browser session, visually navigated the app, identified the bug (demo database mode blocking all writes), and fixed it autonomously.

The model used its own cursor, clicked through features, verified fixes visually, and reported back when all tests passed. This self-correcting loop is what OpenAI CRO Mark Chen describes as computer-use approaching "the same dexterity as manipulating code." (Source: The New Stack)

It's a preview of where agentic AI coding is heading — not just writing code, but verifying it in a real environment and iterating.

Final Thoughts

GPT-5.5 is a strong model for frontend development, desktop apps, games, and knowledge work automation. The benchmark numbers are real — it outperforms Claude Opus 4.7 across most agentic tasks, and the Codex browser-use self-correction loop is a legitimate leap in how AI handles its own output.

But the full-stack app test exposes a gap. When a prompt demands persistent state, backend logic, and complex UI flows all at once, GPT-5.5 needed help that other frontier models didn't. For developers choosing between GPT-5.5 and Claude Opus 4.7 for production codebases, Opus still holds the edge on SWE-Bench Pro.

The model to watch is OpenAI's own GPT-5.5 Pro — once benchmarks for that variant land, the full picture will be clearer. For now, GPT-5.5 earns its position at the frontier for everything except full-stack reliability. If your work is UI/UX, native apps, or knowledge agent workflows, this is the model to use today.