OpenAI has released GPT 5.5. This is a big deal because it is the first completely retrained base model since GPT 4.5. Every model between GPT 5 and GPT 5.4 was a training iteration on the same foundation. According to OpenAI, GPT 5.5 is built from scratch, with a new architecture, a new pretraining corpus, and new agent-oriented objectives — though some independent reviewers note it reads more like a significant post-training upgrade than a ground-up rebuild.

So what is GPT 5.5? It is OpenAI’s model, designed primarily for tasks where an AI needs to plan, execute, self-correct, and keep going without constant human input. OpenAI President Greg Brockman says it can “look at an unclear problem and figure out just what needs to happen next.” This means GPT 5.5 can handle loosely defined tasks the way a senior engineer would.

GPT 5.5 was launched on April 23 for paid subscribers, including users of ChatGPT Plus, Pro, Business, and Enterprise, as well as Codex users. API access will be available soon once OpenAI finalises its cybersecurity measures. One of the standout features of GPT 5.5 is that it ships with a 1-million-token context window in the API, while Codex users get 400K.

But what does “agentic” actually mean in practice?

GPT 5.5 does not just answer your question. It takes a sequence of actions, uses tools, checks its work, and keeps going until the task is finished. You give it a multi-step problem, and it plans a route, executes it, and verifies the result without needing a human to re-prompt at every step.

GPT 5.5 has some numbers

• It scores 82.7% on Terminal-Bench 2.0, which tests command-line workflows.
• It scores 58.6% on SWE-Bench Pro, which evaluates real-world GitHub issue resolution.
• It scores 60 on the Artificial Intelligence Index, putting it at the top of the leaderboard.

There is a real-world example of how GPT 5.5 can be used. OpenAI’s own finance team used Codex running GPT 5.5 to analyse 24,771 K-1 tax forms, totalling 71,637 pages. This accelerated the process by two weeks compared to the previous year.

So how does GPT 5.5 compare to models like Claude Opus 4.7 and Gemini 3.1 Pro?

It depends on the task. GPT 5.5 leads on planning-and-execution tasks, while Claude Opus 4.7 holds the edge on “fix a bug in a real codebase” tasks. Gemini 3.1 Pro Preview comes in at 92.6% on multilingual tasks and competes at a lower price point.

One of the things that matters more than benchmark scores is efficiency. GPT 5.5 is designed to reach outputs with fewer tokens and fewer retries. According to Artificial Analysis, it uses 40% fewer tokens to complete the same Codex tasks as GPT 5.4.

The pricing for GPT 5.5 is $5/$30 per million input/output tokens, which is twice as much as GPT 5.4. The token efficiency gains partially offset this in real workloads. For teams running more than roughly 4 million output tokens per month, a flat ChatGPT Pro plus Codex CLI subscription is likely cheaper than pay-as-you-go API billing.

So what does this mean for software teams and developers?

The real shift with GPT 5.5 is not the benchmark lead but the workflow change it enables — the model is built to act as a “chief of staff” in coding pipelines, something that can be handed a vague brief, decompose it into subtasks, execute them with tools, and verify the output before returning it.

For engineering teams, this means the bottleneck is shifting. You do not need to write a specified prompt to get good output. You need to know how to review and steer output from a model that is already doing work autonomously. This is a different skill from prompt engineering.

Here are some practical tips for using GPT 5.5:

• Use GPT 5.5 for end-to-end feature implementation tasks in Codex, the long-horizon work where previous models required human course-correction mid-task.
• Use it for debugging at scale - OpenAI cites teams cutting debug time “from days to hours” on complex codebases.
• Explore knowledge work automation beyond code, document processing, spreadsheet generation, and research summarisation.
• Route by difficulty — Do not default to GPT 5.5 for everything. Use GPT 5.4 mini or similar for simpler tasks and reserve GPT 5.5 for complex multi-step jobs where the intelligence uplift actually justifies the cost.
• GPT 5.5 still has some gaps. Overall, it is a powerful tool that can help software teams and developers work more efficiently.

The Benchmarks

Honesty is really important when we talk about benchmarks.

GPT 5.5 is not as good as Claude Opus 4.7 on SWE-Bench Pro. This is a test that’s very similar to fixing a real issue on GitHub in a codebase that uses many languages. GPT 5.5 got 58.6% while Claude Opus 4.7 got 64.3%. GPT 5.5 also did not do well on understanding languages. It scored 83.2% on MMMLU while Opus 4.7 scored 91.5%. Gemini 3.1 Pro scored 92.6%. (Note: these figures are sourced from OpenAI’s own reported benchmarks and have not been independently verified at the time of publication.) This is a big difference for teams that work with codebases or users from around the world. They should think about this before they switch to GPT 5.5.

Perhaps the most important weakness to flag is GPT 5.5’s hallucination rate. Independent testing by Artificial Analysis recorded an 86% hallucination rate on their AA-Omniscience evaluation, compared to Claude Opus 4.7 at 36% and Gemini 3.1 Pro Preview at 50%. The model knows more than anything else tested — and it will confidently give a wrong answer at nearly two and a half times the rate of its closest competitor. For production use cases where accuracy matters, this is not a footnote.

The price of the API is also going up. It is not a number in a headline. Teams that use GPT 5.5 a lot should test it with their work and calculate how much it will cost them. Other companies like Anthropic and Google are also working on AI models, and this means that prices might change in the next few months. OpenAI has lowered prices in the past after releasing models.

So is GPT 5.5 the AI model for coding right now?

For coding tasks that’re complex and need a lot of thinking, GPT 5.5 is probably the best. For fixing bugs in existing codebases, Claude Opus 4.7 is still better. For teams that’re careful about costs, Gemini 3.1 Pro Preview is also a good option. The market for AI models is very competitive now. No single model is the best in every category.

One thing is clear: GPT 5.5 is a step forward in AI technology. It was built from scratch. Can handle complex coding tasks on its own. Whether it is worth the extra cost depends on how you use it. You should test it carefully before you decide to switch.

The best AI model is not always the one that does the best on benchmarks. It is the one that is best suited for what you need to do.

TL;DR

GPT 5.5 is OpenAI’s AI model, and it was built to handle coding tasks on its own. It does well on benchmarks and uses fewer tokens, and it is good at solving complicated problems. It costs twice as much as the old model, but it is more efficient. For fixing bugs, Claude Opus 4.7 is still better. For everything else, GPT 5.5 is the best coding AI right now.

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