Introducing GPT-5.5

We’re releasing GPT‑5.5, our smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer.

GPT‑5.5 understands what you’re trying to do faster and can carry more of the work itself. It excels at writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished. Instead of carefully managing every step, you can give GPT‑5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going.

The gains are especially strong in agentic coding, computer use, knowledge work, and early scientific research—areas where progress depends on reasoning across context and taking action over time. GPT‑5.5 delivers this step up in intelligence without compromising on speed: larger, more capable models are often slower to serve, but GPT‑5.5 matches GPT‑5.4 per-token latency in real-world serving, while performing at a much higher level of intelligence. It also uses significantly fewer tokens to complete the same Codex tasks, making it more efficient as well as more capable.

We are releasing GPT‑5.5 with our strongest set of safeguards to date, designed to reduce misuse while preserving access for beneficial work. We evaluated this model across our full suite of safety and preparedness frameworks, worked with internal and external redteamers, added targeted testing for advanced cybersecurity and biology capabilities, and collected feedback on real use cases from nearly 200 trusted early-access partners before release.

Today, GPT‑5.5 is rolling out to Plus, Pro, Business, and Enterprise users in ChatGPT and Codex, and GPT‑5.5 Pro is rolling out to Pro, Business, and Enterprise users in ChatGPT. API deployments require different safeguards and we are working closely with partners and customers on the safety and security requirements for serving it at scale. We'll bring GPT‑5.5 and GPT‑5.5 Pro to the API very soon.

| Benchmark | GPT-5.5 | GPT-5.4 | Competitors | | :--- | :---: | :---: | :---: | | Terminal-Bench 2.0 | 82.7% | 75.1% | 68.5% - 69.4% | | Expert-SWE (Internal) | 68.5% | - | - | | GDPval (wins or ties) | 83.0% | 82.3% | 67.3% - 80.3% | | OSWorld-Verified | 75.0% | - | 78.0% | | FrontierMath Tier 4 | 35.4% | 27.1% | 16.7% - 22.9% | | CyberGym | 79.0% | - | 73.1% |

OpenAI is building the global infrastructure for agentic AI, making it possible for people and businesses around the world to get work done with AI. Over the past year, we’ve seen AI dramatically accelerate software engineering. With GPT‑5.5 in Codex and ChatGPT, that same transformation is beginning to extend into scientific research and the broader work people do on computers.

Across these domains, GPT‑5.5 is not just more intelligent; it is more efficient in how it works through problems, often reaching higher-quality outputs with fewer tokens and fewer retries. On Artificial Analysis's Coding Index, GPT‑5.5 delivers state-of-the-art intelligence at half the cost of competitive frontier coding models.

GPT‑5.5 is our strongest agentic coding model to date. On Terminal-Bench 2.0, which tests complex command-line workflows requiring planning, iteration, and tool coordination, it achieves a state-of-the-art accuracy of 82.7%. On SWE-Bench Pro, which evaluates real-world GitHub issue resolution, it reaches 58.6%, solving more tasks end-to-end in a single pass than previous models. On Expert-SWE, our internal frontier eval for long-horizon coding tasks with a median estimated human completion time of 20 hours, GPT‑5.5 also outperforms GPT‑5.4.

Across all three evals, GPT‑5.5 improves on GPT‑5.4’s scores while using fewer tokens. The model’s coding strengths show up especially clearly in Codex where it can take on engineering work ranging from implementation and refactors to debugging, testing, and validation. Early testing suggests GPT‑5.5 is better at the behaviors real engineering work depends on, like holding context across large systems, reasoning through ambiguous failures, checking assumptions with tools, and carrying changes through the surrounding codebase.