OpenAI launched its latest frontier model, GPT-5.2, on Thursday amid growing competition from Google, touting it as its most advanced model yet and designed for developers and everyday professional use.
OpenAI’s GPT-5.2 is coming to paid ChatGPT users and developers via the API in three versions: Instant, a speed-optimized model for routine queries such as searching for information, writing, and translating; Reflective, who excels at complex structured work like coding, analyzing long documents, math, and planning; and Pro, the high-end model aimed at providing maximum precision and reliability for difficult problems.
“We designed version 5.2 to deliver even more economic value to users,” Fidji Simo, OpenAI’s chief product officer, said in a briefing with reporters on Thursday. “It’s best to create spreadsheets, create presentations, write code, perceive images, understand long context, use tools, and then connect complex projects in multiple steps.”
GPT-5.2 lands in the middle of an arms race with Google’s Gemini 3, which tops LMArena’s rankings in most benchmarks (except for coding – which Anthropic’s Claude Opus-4.5 has always locked down).
Earlier this month, The Information reported that CEO Sam Altman issued an internal “code red” memo to staff amid falling ChatGPT traffic and fears of losing market share to Google. Code Red called for a shift in priorities, including stopping on commitments like introducing ads and instead focusing on creating a better ChatGPT experience.
GPT-5.2 is OpenAI’s push to regain its leadership, although some employees have reportedly requested that the model release be pushed back so the company can have more time to improve it. And despite indications that OpenAI would focus its attention on consumer use cases by adding more customization to ChatGPT, the launch of GPT-5.2 appears to strengthen its enterprise opportunities.
The company specifically targets developers and the tools ecosystem, aiming to become the default base for building AI-driven applications. Earlier this week, OpenAI released new data showing that enterprise use of its AI tools has increased significantly over the past year.
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This comes as Gemini 3 has become tightly integrated into Google’s product and cloud ecosystem for multi-modal and agent workflows. Google this week launched managed MCP servers that make it easier for agents to connect its Google and Cloud services like Maps and BigQuery. (MCPs are the connectors between AI systems, data and tools.)
OpenAI claims that GPT-5.2 sets new benchmarks in coding, math, science, vision, long-context reasoning, and tool usage, which the company says could lead to “more reliable agent workflows, production-quality code, and complex systems that operate across large contexts and real-world data.”
These capabilities put it in direct competition with Gemini 3’s Deep Think mode, which has been touted as a major advancement in reasoning targeting math, logic, and science. On OpenAI’s own benchmark chart, GPT-5.2 Thinking edges out Anthropic’s Gemini 3 and Claude Opus 4.5 in almost every reasoning test listed, from real-world software engineering tasks (SWE-Bench Pro) and doctoral-level science knowledge (GPQA Diamond) to abstract reasoning and pattern discovery (ARC-AGI suites).
Aidan Clark, head of research, said better results in mathematics are about more than just solving equations. Mathematical reasoning, he explained, helps determine whether a model can follow multi-step logic, maintain consistent numbers over time and avoid subtle errors that could compound over time.
“These are all properties that really matter across a wide range of different workloads,” Clark said. “Things like financial modeling, forecasting, data analysis.”
During the briefing, OpenAI product manager Max Schwarzer said GPT-5.2 “brings substantial improvements to code generation and debugging” and can navigate complex math and logic step by step. Coding startups like Windsurf and CharlieCode, he added, are reporting “state-of-the-art agent coding performance” and measurable gains on complex, multi-step workflows.
Beyond coding, Schwarzer said GPT-5.2 Thinking Answers contains 38% fewer errors than its predecessor, making the model more reliable for everyday decision-making, research and writing.
GPT-5.2 appears to be less of a reinvention and more of a consolidation of OpenAI’s last two upgrades. GPT-5, dropped in August, was a reset that laid the foundation for a unified system with a router to switch the model between a fast default model and a deeper “Thinking” mode. November’s GPT-5.1 aimed to make this system warmer, more conversational, and better suited to agent and coding tasks. The latest model, GPT-5.2, appears to improve on all of these advancements, making it a more reliable basis for production use.
For OpenAI, the stakes have never been higher. The company has made $1.4 trillion in commitments for AI infrastructure development over the next few years to support its growth – commitments it made while it still had a first-mover advantage among AI companies. But now that Google, which was an early laggard, is moving forward, this bet could be the origin of Altman’s “code red.”
OpenAI’s renewed interest in reasoning models is also a risky approach. The systems behind its deep thinking and research modes are more expensive to run than standard chatbots because they require more computing. By doubling down on this type of model with GPT-5.2, OpenAI could create a vicious cycle: spending more on compute to win the rankings, then spending even more to keep these expensive models running at scale.
OpenAI is already reportedly spending more on computing than it previously let on. As TechCrunch recently reported, most of OpenAI’s inference expenses (money spent on compute to run a trained AI model) are paid in cash rather than cloud credits, suggesting that the company’s compute costs have increased beyond what partnerships and credits can subsidize.
During the call, Simo suggested that as OpenAI scales, it is able to offer more products and services to generate more revenue to pay for additional computing.
“But I think it’s important to put this within the grand arc of effectiveness,” Simo said. “Today you get a lot more intelligence for the same amount of computing and the same amount of money as you did a year ago. »
Despite the emphasis on reasoning, one thing missing from today’s launch is a new image generator. Altman reportedly said in his code red memo that image generation would be a key priority going forward, especially after Google’s Nano Banana (the nickname for Google’s Gemini 2.5 Flash Image model) had a viral moment following its release in August.
Last month, Google launched Nano Banana Pro (aka Gemini 3 Pro Image), an improved version with even better text rendering, world awareness, and a weird, real, never-before-seen vibe to its photos. It also integrates better into Google products, as demonstrated last week by its appearance in tools and workflows such as Google Labs Mixboard for automated presentation generation.
OpenAI reportedly plans to launch another new model in January with better images, improved speed and better personality, although the company did not confirm those plans on Thursday.
OpenAI also announced the rollout of new security measures regarding the use of mental health and teen age verification on Thursday, but did not spend much of the launch showcasing these changes.
This article has been updated with more information on OpenAI’s computational efficiency status.
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