GPT-5.5 is OpenAI’s most capable agentic AI model yet
OpenAI launched GPT-5.5 on April 23 as what it calls “a new class of intelligence for real work and powering agents,” and the framing is deliberate. OpenAI says it’s the most capable agentic AI model to date, built from the ground up to plan, use tools, check its own output, and work through tasks independently. […] The post GPT-5.5 is OpenAI’s most capable agentic AI model yet appeared first on AI News .
OpenAI launched GPT-5.5 on April 23 as what it calls “a new class of intelligence for real work and powering agents,” and the framing is deliberate. OpenAI says it’s the most capable agentic AI model to date, built from the ground up to plan, use tools, check its own output, and work through tasks independently. 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 tasks e (Image source: “‘The Agent’ Fossil Watch” by MarkGregory007 is licensed under CC BY-NC-SA 2.0. ) Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo . Click here for more information. AI News is powered by TechForge Media . Explore other upcoming enterprise technology events and webinars here . The post GPT-5.5 is OpenAI’s most capable agentic AI model yet appeared first on AI News .
Key takeaways
- GPT-5.5 represents a significant advancement in agentive AI, with potential to optimize operations in Brazilian companies.
- Transparency in performance benchmarks can facilitate AI adoption in corporate environments in Brazil.
- The increase in API usage costs may be a barrier for small and medium enterprises, requiring careful cost-benefit analysis.
Editorial analysis
The launch of GPT-5.5 represents a significant advancement in the capabilities of agentive AI models, especially in a context where efficiency and autonomy are increasingly valued. For the Brazilian tech sector, this could open new opportunities, particularly in areas such as process automation and software development, where delegating complex tasks to AI can reduce costs and increase productivity. The GPT-5.5's ability to perform tasks with fewer human instructions could be a game changer for companies looking to optimize their operations.
Moreover, the implementation of rigorous benchmarks, such as Terminal-Bench 2.0 and Expert-SWE, indicates OpenAI's commitment to providing clear performance metrics. This is crucial for the adoption of AI in corporate environments, where data-driven decisions are essential. For Brazil, where many companies are still in the early stages of AI adoption, transparency in results can facilitate trust and acceptance of these technologies.
However, the issue of costs also deserves attention. The increase in API usage fees may be a barrier for small and medium enterprises, which may not have the same investment capacity as large corporations. OpenAI argues that the efficiency of GPT-5.5 offsets these costs, but it will be important to observe how this translates into practice, especially in a Brazilian market where cost-benefit relationships are a decisive factor for adopting new technologies.
Finally, competition with other models, such as Claude Opus 4.7, highlights the need for continuous innovation. OpenAI must continue to enhance its offerings to maintain its leadership position. For Brazil, this means that companies should stay alert to global trends in AI and consider how they can benefit from emerging innovations, ensuring they do not fall behind in a rapidly evolving technological landscape.
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