OpenAI GPT‑5.5: Faster, Smarter, and Built to Actually Finish Your Work

OpenAI’s new GPT‑5.5 model is built less for casual chatting and more for actually getting things done. It’s designed to handle messy, multi-step workflows end to end: planning, using tools, checking its own work, and finishing the job with fewer retries and less back-and-forth.

From complex coding and game development to research, documents, and browser automation, GPT‑5.5 is positioned as a serious upgrade over previous GPT models and a strong competitor to models like Claude Opus 4.7 and Gemini 3.1.

What’s New in GPT‑5.5?

GPT‑5.5 focuses on being an end-to-end problem solver rather than just a question-answering bot. Its biggest improvements show up in:

Multi-step, real-world workflows

• Plans and executes long tasks instead of just responding to single prompts.
• Uses tools, checks results, and iterates to reach a working solution.
• Much better at “agentic” behavior – acting more independently over time.

Coding, research, and data work

• Handles large codebases with long context and complex dependencies.
• Improved at debugging, refactoring, testing, and validating its own assumptions.
• Stronger at research-style tasks: gathering information, summarizing, and turning it into documents, spreadsheets, and presentations.

Document and software workflows

• Can take a knowledge task from research → outline → draft → polished document.
• Builds structured spreadsheets and slide decks with good context awareness.
• Controls software and browsers more reliably for automation-style workflows.

Benchmarks, Token Efficiency, and Cost

On paper, GPT‑5.5 is now at or near state-of-the-art on several real-world coding and reasoning benchmarks, especially those that test full workflows instead of isolated questions.

Key benchmark highlights

• TerminalBench (complex command-line workflows): GPT‑5.5 scores around 82.7%, outperforming other leading models by a noticeable margin.
• SWE-bench Verify (solving real GitHub issues end to end): GPT‑5.5 scores about 58.6%. Claude Opus 4.7 edges it out slightly on this specific benchmark, but that’s not the whole story.

The crucial difference is efficiency:

• GPT‑5.5 uses roughly ¼ the tokens of GPT‑5.4-high for similar tasks.
• It uses about ⅓ the tokens of Claude Opus 4.7 for the same input and output.
• It usually needs fewer steps, fewer retries, and less back-and-forth to reach a correct answer.

Because different models tokenize text differently, raw benchmark scores don’t tell the whole story. In real-world coding workflows, GPT‑5.5 often ends up being faster, more consistent, and more cost-efficient at actually completing tasks end to end.

On broader intelligence measures, GPT‑5.5 sits near the top of the AI frontier, delivering very strong performance at roughly half the effective cost of some competing frontier models when you factor in token savings. For more context on how Claude’s latest models compare, see our breakdown of Claude Opus 4.7.

Pricing

GPT‑5.5 is not cheap, but it aims to make up for that with efficiency:

• Input: $5 per 1M tokens
• Output: $30 per 1M tokens
• Cached tokens: $0.50 per 1M cached tokens

This is roughly 20% more expensive than Opus 4.7 on a pure per-token basis, but the model often finishes tasks using far fewer tokens overall.

Hands-On: Coding, Games, and Front-End Power

Where GPT‑5.5 really shines is when it’s paired with a “harness” – tools that let it act like an autonomous coding agent. The transcript highlights two in particular: Kilo CLI (an open-source coding agent) and Codeex (an intelligent harness for building and running projects).

End-to-End Engineering with Codeex and Kilo

Inside tools like Codeex or Kilo, GPT‑5.5 can:

• Take on full engineering tasks from scratch.
• Implement features, refactor code, and fix bugs.
• Write and run tests, then adjust based on failures.
• Use multiple tools and propagate changes across an entire system.

It’s especially strong at:

• Holding context across large codebases.
• Reasoning through ambiguous or flaky failures.
• Checking its own assumptions and revising its approach.

MacOS-Style UI and Game Clones

One of the most striking demos is a MacOS-style browser UI built by GPT‑5.5:

• A full desktop-like interface with brightness and volume controls.
• An app dock with accurately styled SVG icons (Safari, Mail, Maps, Notes, Photos, FaceTime, Calendar, Reminders, and more).
• All generated from a single high-level request.

Inside that MacOS clone, GPT‑5.5 also built a simple Minecraft-style game:

• Block placement and breaking.
• Basic cave systems and ore generation.
• Water dynamics and simple physics.

Using Kilo CLI with GPT‑5.5 at its highest reasoning level, it also generated a CS:GO-like 3D shooter in a few minutes:

• A full map with allies, enemies, and checkpoints.
• Shooting mechanics with cooldowns.
• Minimap, animations, textures, and even an in-game store.

Another user prompt produced a more advanced standalone Minecraft clone with:

• Infinite terrain generation.
• Water that’s harder to traverse and feels more physical.
• Block-breaking animations and smoother gameplay.

The pattern is clear: when you give GPT‑5.5 detailed, well-structured instructions, it can deliver surprisingly complete games and interactive experiences. Vague prompts, on the other hand, lead to weaker results.

3D and Physics with Three.js

GPT‑5.5 is also strong in the 3D web space. In one test, it was asked to build an off-road 3D physics simulation of an SUV driving over rough terrain using Three.js:

• Generated detailed terrain with rocks, hills, and mountains.
• Created a 3D SUV model and motion.
• Produced a visually rich simulation with realistic-feeling movement.

However, it’s not perfect. For example, when asked to build a 360° product viewer, it produced a decent but generic front-end and failed to create a true 3D product viewer. Competing models like Gemini sometimes do better on that specific type of prompt.

SVG, Visuals, and GPT Image 2 Integration

GPT‑5.5 is particularly impressive at SVG-based visuals, often outperforming other models in this niche.

SVG illustrations and icons

• Generated detailed SVG butterflies and abstract paintings with strong structure and style.
• Created console-style controllers (PS5, Xbox) in SVG with accurate shapes and layout.
• Occasional quirks (like slightly misplaced rocks in a landscape), but overall high quality.

In one case, GPT‑5.5 first used the GPT Image 2 model to generate a realistic PS5 controller image, then converted that into an SVG. The structure and proportions were impressively accurate for a purely code-based vector drawing.

Game and UI assets via GPT Image 2

Because GPT‑5.5 can work alongside GPT Image 2, you can:

• Generate high-quality textures, game assets, and UI elements from text prompts.
• Apply those textures directly inside tools like Codeex.
• Dynamically create visuals during development or even at runtime.

If you want to go deeper on GPT Image 2 itself, we’ve covered its capabilities and use cases in detail in our GPT Image 2 guide.

Front-End and Knowledge Work Performance

Beyond games and 3D, GPT‑5.5 is very strong at classic front-end and knowledge work.

Landing pages and dashboards

• Handles long, detailed prompts for landing pages with multiple sections, animations, and typography styles.
• Produces layouts with dynamic movement, distinct components, and good visual hierarchy.
• Can insert realistic placeholders and structure for real-world apps.

For example, when asked to build a CRM dashboard in the ChatGPT web app with the highest “extended thinking” mode enabled, GPT‑5.5:

• Used appropriate charting and UI packages.
• Generated a polished, production-style dashboard layout.
• Included tables, filters, charts, and navigation that felt cohesive.

End-to-end knowledge workflows

On the non-coding side, GPT‑5.5 is strong at:

• Researching topics across the web (when tools are enabled).
• Summarizing and structuring information.
• Producing documents, spreadsheets, and presentation outlines that stay on-topic and consistent.

It’s built to handle the full loop: research → analysis → structured output, not just one-off answers.

How to Access GPT‑5.5

You can start using GPT‑5.5 in a few different ways:

1. ChatGPT (web/app)

• Available to all paid ChatGPT users.
• In your model settings, select the GPT‑5.5 “thinking” model to enable extended reasoning.
• Use it directly for chat, coding, and document workflows.

2. OpenAI API

• Access GPT‑5.5 via the standard OpenAI API with the pricing mentioned above.
• Ideal for integrating into your own apps, agents, or internal tools.

3. Through third-party harnesses (e.g., Kilo, Codeex)

• Kilo CLI offers an open-source coding agent setup and provides free API credits (around $25) to get started.
• Codeex acts as an intelligent harness for building, running, and iterating on complex projects with GPT‑5.5 as the engine.
• These setups are where GPT‑5.5’s agentic strengths really show: long-horizon coding, refactors, debugging, and multi-tool workflows.

Is GPT‑5.5 Worth Using?

GPT‑5.5 is more expensive per token than some competitors, but it’s also more token-efficient and better at finishing complex tasks with fewer retries. For many real-world use cases, that means lower effective costs and faster turnaround.

It stands out if you:

• Do serious coding work (especially with large or messy codebases).
• Need end-to-end workflows (research → code → tests → docs).
• Care about front-end quality, SVG visuals, or 3D/Three.js projects.
• Want an agent-like model that can operate inside tools like Kilo or Codeex.

It’s not perfect—some 3D and product-viewer prompts still lag behind certain rivals—but overall GPT‑5.5 is a major step forward in practical capability. For many developers and power users, it’s likely to become the new default “workhorse” model.