OpenAI just released GPT-5.5, and it looks like a pretty big update, especially if you care about coding, agents, and computer use.
It writes code better, uses computers more reliably, and can handle more agentic work without you constantly checking on it.
The model is rolling out today to Plus, Pro, Business, and Enterprise users inside ChatGPT and Codex. There is also a stronger version called GPT-5.5 Pro, which is coming to Pro, Business, and Enterprise users.
For developers, API access is listed as “coming soon.” The sad part is the price. It is double the per-token price of GPT-5.4, which is probably the number many developers will argue about this week.
Okay, if you have been following me here on Medium, you already know I cover OpenAI releases pretty often. But GPT-5.5 is the first one in a while that feels like more than just another version number.
According to OpenAI, it is the first fully retrained base model since GPT-4.5. The jump in agentic coding benchmarks also seems to back that up.
Let’s get into the details.
What GPT-5.5 actually is
GPT-5.5 is a frontier model built for longer and messier tasks. The main idea is simple. It should need less hand-holding from the user.
OpenAI says it was designed for agentic coding, computer use, knowledge work, and early scientific research. In practice, that means you can give it a broad task and expect it to plan, use tools, check its own work, and keep going instead of stopping every few steps.
It is now available on the ChatGPT app both on the desktop and browser.
GPT 5.5 in ChatGPT. Image by Jim Clyde Monge
The release comes with two versions.
The standard GPT-5.5 model is the default option for most production workloads. GPT-5.5 Pro uses parallel test-time compute on the same underlying model to push accuracy higher on harder problems. That version is aimed more at research synthesis, technical analysis, and regulated-domain work where a wrong answer can cost more than an expensive API call.
The context window is also huge. GPT-5.5 supports 1 million tokens in the API and 400K tokens in Codex. That is enough to drop an entire mid-sized codebase into one prompt, which used to be the kind of thing people had to fake with retrieval pipelines and clever chunking.
How it performs on benchmarks
The coding numbers are getting most of the attention, and honestly, that makes sense.
On Terminal-Bench 2.0, which tests command-line agentic workflows, GPT-5.5 scored 82.7%. GPT-5.4 scored 75.1%. That puts GPT-5.5 at the top among publicly available models.
GPT 5.5 coding benchmark. Image by Jim Clyde Monge
Anthropic’s Claude Opus 4.7 sits at 69.4%, while Google’s Gemini 3.1 Pro lands at 68.5% on the same test, according to reporting from The Decoder.
The other coding results tell a similar story.
On OpenAI’s internal Expert-SWE benchmark, which tests long engineering tasks with a median human completion time of around 20 hours, GPT-5.5 reached 73.1%. GPT-5.4 scored 68.5%.
On SWE-Bench Pro, which grades real-world GitHub issue resolution, GPT-5.5 reached 58.6%. Claude Opus 4.7 actually beats it on that specific test with 64.3%, though OpenAI noted that Anthropic had reported signs of memorization on a subset of problems.
Outside of coding, a few numbers stood out to me:
GDPval at 84.9%: This measures performance across 44 real occupations, including finance and legal research. GPT-5.5 matches or beats industry professionals in that percentage of comparisons.
GPT 5.5 professional benchmark. Image by Jim Clyde Monge
FrontierMath Tier 4 at 35.4% for the standard model and 39.6% for Pro: This is the hardest tier of a math benchmark built by working mathematicians and designed to resist memorization.
BrowseComp at 90.1% for Pro: This tests how well a model can find hard-to-locate information on the web.
OSWorld-Verified at 78.7%: This is a desktop computer-use benchmark where the model has to click through real UIs and complete mixed software tasks.
OpenAI also says GPT-5.5 now tops the Artificial Analysis Intelligence Index with a score of 60. That puts it three points ahead of Claude Opus 4.7 and Gemini 3.1 Pro Preview, which were both tied at 57.
GPT 5.5 Artificial Analysis Index benchmark. Image by Jim Clyde Monge
The frontier model race has been pretty tight this year, especially between OpenAI, Anthropic, and Google. This release puts OpenAI back in front, at least on the public benchmark board.
But there is one honest caveat here.
On GDPval, the improvement over GPT-5.4 is not huge. GPT-5.5 scored 84.9%, while GPT-5.4 scored 83.0%. If that benchmark really measures everyday professional work well, then GPT-5.5 may not feel like a massive jump for normal ChatGPT tasks.
GPT 5.5 GDPval benchmark. Image by Jim Clyde Monge
The coding and agentic improvements are much more convincing.
Token efficiency and speed
Usually, better performance means slower responses or higher costs. Sometimes both.
That is why GPT-5.5 is interesting. OpenAI says the model matches GPT-5.4’s per-token latency in real-world serving while using far fewer tokens to complete the same Codex tasks.
The company claims around a 40% reduction in output token usage on comparable work. That is a real efficiency gain. It also helps soften the blow of the doubled per-token price, at least for some workflows.
There is also a new Fast mode in Codex. It generates tokens 1.5x faster at 2.5x the cost. That is clearly aimed at interactive coding sessions where waiting around for the model can ruin the flow.
GPT 5.5 in Codex. Image by Jim Clyde Monge
The release also includes five effort levels, ranging from non-reasoning up to “xhigh.” That gives developers more control over how much they want to spend depending on the task.
For quick fixes, you probably do not need the most expensive setting. For deep debugging or long coding tasks, the higher effort levels might make sense.
Right now, I am still waiting for other coding tools, Warp and Antigravity, to add support for GPT 5.5.
Pricing, and why people are talking about it
This is the part that will probably dominate the discussion on Hacker News.
GPT 5.5 pricing comparison against older models. Image by Jim Clyde Monge
The standard GPT-5.5 API will cost $5 per million input tokens and $30 per million output tokens. That is exactly double the price of GPT-5.4, which was $2.50 input and $15 output.
