GPT-5.5 is OpenAI’s most capable agentic AI model yet

Last updated: August 22, 2026

GPT-5.5 is OpenAI’s most capable agentic AI model yet

OpenAI released GPT-5.5 on April 23 as its most capable agentic model, designed to plan, use tools, and self-correct across complex tasks. Co-developed with NVIDIA's GB200/GB300 systems, it reduces the need for human course-correction. Rolling out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex, with API access from April 24.

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GPT-5.5 is the first retrained base model since GPT-4.5, co-designed with NVIDIA’s GB200 and GB300 NVL72 rack-scale systems. The company says the practical difference is that when using GPT5.5, tasks that previously required multiple prompts and human ‘course-correction’ can now be handed off more completely. The model is rolling out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex. API access followed on April 24.

The benchmarks

OpenAI’s strongest performance claim is on Terminal-Bench 2.0, a benchmark that tests command-line workflows requiring planning and tool coordination in a sandboxed environment. GPT-5.5 scores 82.7%, against GPT-5.4’s 75.1% and Claude Opus 4.7’s 69.4%.

On SWE-Bench Pro, which evaluates GitHub issue resolution, GPT-5.5 reaches 58.6%, solving more issues in a single pass than previous versions. OpenAI also introduced Expert-SWE, an internal benchmark where tasks carry a median estimated human completion time of 20 hours. GPT-5.5 scores 73.1%, up from GPT-5.4’s 68.5%.

In long-context reasoning, MRCR v2 at one million tokens, a retrieval benchmark testing whether a model can locate a specific answer buried in a large document, GPT-5.5 scores 74.0%, against GPT-5.4’s 36.6%.

However, on MCP Atlas, Scale AI’s Model Context Protocol tool-use benchmark, Claude Opus 4.7 leads at 79.1% and no score is recorded by GPT-5.5. OpenAI included that absence in its own benchmark table, which at least signals its confidence in the overall picture.

Token efficiency, pricing reality

API access is priced at US$5 per million input tokens and US$30 per million output tokens, exactly twice the rates for GPT-5.4. OpenAI’s defence is that GPT-5.5 completes the same Codex tasks with fewer tokens than GPT-5.4, making effective costs roughly 20% higher once its efficiency is factored in, a claim that independent testing lab Artificial Analysis validated.

GPT-5.5 Pro, available to Pro, Business, and Enterprise users, is priced at US$30 per million input tokens and US$180 per million output tokens. It applies additional parallel test-time compute on harder problems and leads the list of publicly-available models on BrowseComp, OpenAI’s agentic web-browsing benchmark, at 90.1%.

Token efficiency is worth stress-testing against actual workloads before committing to a model switch. At 10 million output tokens per month, GPT-5.5 standard costs US$300 against Claude Opus 4.7’s US$250, a 20% that only pays off if the model’s superior agentic performance means fewer task iterations and fewer retries, with the maths varying by use case.

In practice

Open AI says more than 85% of employees now use Codex weekly in their departments, including engineering and marketing. In one example, the communications team used GPT-5.5 to process six months of speaking request data, where the model was able to build a scoring and risk framework to help automate low-risk approvals.

Greg Brockman described the release as “a real step forward towards the kind of computing that we expect in the future,” and chief scientist Jakub Pachocki noted the last two years of model progress had felt “surprisingly slow.”

OpenAI says GPT-5.5 matches GPT-5.4’s per-token latency in production serving while performing at a higher level of intelligence; larger, more capable models are often slower to serve, but that trade-off was avoided here.

Whether the benchmark leads translate into production gains for teams running real agentic pipelines is the question that will take the next few weeks to answer properly. The Terminal-Bench score is promising for unattended terminal agents and DevOps automation. The MCP Atlas gap is worth watching for anyone building heavily on tool-use orchestration.

See also: OpenAI brings GPT-5.5 to Codex for coding taskse

(Image source: “‘The Agent’ Fossil Watch” by MarkGregory007 is licensed under CC BY-NC-SA 2.0.)

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