Nearly a month after Google launched its flagship model, Gemini 3, OpenAI’s announcement of GPT-5.2 has reshaped the competitive landscape of advanced artificial intelligence models. The race has moved beyond merely improving accuracy or boosting benchmark scores, shifting instead toward redefining development priorities and steering technological investments—at a time when AI models have become a core component of analytical infrastructures within large organizations.In this context, GPT-5.2 holds particular significance for the field of data analytics. The advancement of its language and reasoning capabilities opens broader horizons for extracting complex patterns, linking interdependent variables, and efficiently handling massive volumes of unstructured data. This progress is driving a tangible transformation in how data is understood and how analytical insights are built, directly influencing the way decision support is delivered in data-driven business environments.In this article, we explore the details of the GPT-5.2 launch, its new models, and their advanced capabilities in reasoning, programming, and long-context processing—now available to ChatGPT subscribers and through the API.

First, what GPT-5.2 models are available?

OpenAI has introduced ChatGPT GPT-5.2 as a family of three distinct versions—not merely as a scale of increasing power, but as a deliberate distribution of roles within modern work environments. Understanding this structure is important for data analysts and organizations alike, as it clarifies when speed is required, when depth is essential, and when reliability becomes the top priority.

1. GPT-5.2 Instant: Speed First

The GPT-5.2 Instant version focuses on minimizing response time and delivering immediate results. It is positioned as the daily workhorse for lightweight tasks such as quick information retrieval, text drafting, translation, and simple automation. This is the version most users will interact with by default, as it prioritizes fast processing over deep reasoning.In practice, this model fills a clear gap in analytical work environments when rapid answers or light automation are needed without invoking deep reasoning. It is well suited for initial exploratory questions or routine tasks that do not require multi-step thinking.

2. GPT-5.2 Thinking: Methodical Depth in Problem Solving

GPT-5.2 Thinking is designed to meet the need for careful, deliberate analysis. It is equipped with expanded reasoning capabilities that allow the model to process complex problems step by step before delivering a final outcome. According to OpenAI’s internal benchmarks, this version delivers the highest performance in knowledge work, programming, and long-context handling—especially when used alongside tools such as spreadsheets and presentations.This model represents a serious attempt to build a general engine for cognitive work. It is the preferred choice whenever accuracy improves through deliberate and structured reasoning. For many organizations, GPT-5.2 Thinking will serve as the backbone for analytical tasks, multi-step workflows, and agentic tasks that require logical coherence and internal validation of results.

3. GPT-5.2 Pro: Reliability in High-Risk Environments

GPT-5.2 Pro is the flagship version, primarily targeted at enterprise customers. It is the most expensive option in the lineup, as it is designed for high-sensitivity scenarios where errors carry significant costs. This version addresses the needs of teams that require a model capable of maintaining consistency across very long contexts.As a result, GPT-5.2 Pro is well suited for decision-support systems, complex planning, and any workload in which reliability is as critical as analytical strength itself.Through this segmentation, OpenAI does not offer a one-size-fits-all model. Instead, it provides an ecosystem of models that enables data analysts and organizations to select the most appropriate tool for each level of work—from operational speed, to deep analysis, and ultimately to high-stakes strategic decision-making.

What’s New in GPT-5.2?

GPT-5.2 represents a qualitative leap in how artificial intelligence models handle professional and knowledge-intensive tasks—particularly through GPT-5.2 Thinking, which is designed for deep reasoning and multi-step workflows.Indicators released by OpenAI show that this version has moved beyond the role of a smart assistant to a level of performance that matches—and in many cases surpasses—established human expertise in critical fields. Below are the key advancements introduced with this model:

  • Near-Expert Cognitive Performance

GPT-5.2 Thinking marks a genuine breakthrough in professional cognitive tasks. This is reflected in its performance on the GDPval benchmark, which evaluates models across specific tasks spanning 44 different professions. The model achieved a score of 70.9%, a result indicating parity with—or superiority to—industry experts based on direct human evaluations. The significance of this figure becomes clearer when compared to GPT-5.1 Thinking, which scored only 38.8% on the same tasks. This gap highlights a clear shift from a supportive model to one capable of competing with human expertise in knowledge work.

  • Operational Efficiency That Redefines Productivity

Progress is not limited to output quality; it also extends to economic efficiency. Across its different versions, GPT-5.2 has demonstrated the ability to complete professional tasks more than 11 times faster than human experts, at less than 1% of the cost of human execution. These figures reflect a fundamental shift in how productivity is conceived, making large-scale analytical and knowledge work possible without proportional increases in cost or time.

  • Advanced Leadership in Software Engineering and Science

In software engineering, the model achieved a score of 55.6% on the SWE-Bench Pro test—one of the most demanding benchmarks for assessing real-world programming challenges.In the scientific domain, GPT-5.2 Pro scored 93.2% on the GPQA Diamond benchmark, which targets graduate-level questions, while the Thinking version achieved 92.4%.These results place the model at a level comparable to specialized academics and researchers.

  • Higher Reliability and Deeper Long-Context Understanding

One of the most notable improvements is a 30% reduction in hallucination rates compared to GPT-5.1 Thinking, based on real-world usage data from ChatGPT. The model also demonstrated near-perfect performance on the MRCR test for long-context retrieval, handling up to 256,000 tokens. This ensures information coherence and reasoning accuracy even when working with lengthy and complex documents or analyses.

  • Advanced Visual Understanding for Data-Driven Analysis

Beyond textual reasoning, GPT-5.2 Thinking delivers the most advanced visual capabilities developed by OpenAI to date. It reduced error rates by nearly half in chart reasoning tasks and in understanding software interfaces. Practically, this enables more accurate interpretation of dashboards, screenshots, technical diagrams, and visual reports—supporting fields such as finance, operations, engineering, design, and customer support.

  • More Precise Scientific Reasoning for Research Visuals

Results from benchmarks such as CharXiv Reasoning confirm the model’s ability to deeply analyze scientific figures and research diagrams—not merely as visual elements, but as sources of knowledge carrying meaning and conclusions. This advancement makes GPT-5.2 a powerful tool for data analysts and researchers who rely heavily on scientific reports and dense visual representations in their daily work.Overall, GPT-5.2 does not offer a single-dimensional improvement. Instead, it delivers an integrated suite of cognitive, economic, and visual capabilities that redefine the boundaries of data-driven analysis and professional knowledge work.

GPT-5.2 Versus Competitors: An Analytical Reading of the Balance of Power

To understand GPT-5.2’s position in today’s AI landscape, it must be compared with the most prominent competing releases, most notably Google’s Gemini 3 and Anthropic’s Claude Opus 4.5. This comparison is not merely about numbers, but about the philosophy guiding the development of each model and the types of tasks in which each one excels.

  • GPT-5.2 vs. Gemini 3

Gemini 3 was launched in mid-November and succeeded in leading several of the most widely followed global benchmarks. It ranked first in the Humanity’s Last Exam test and slightly outperformed GPT-5.2 Pro on the GPQA Diamond benchmark (93.8% versus 93.2%).This edge is likely due to substantial improvements in the application of the Mixture-of-Experts architecture, along with an advanced training approach based on token-level sparse routing, supported by Google’s custom TPU infrastructure.By contrast, GPT-5.2 shows clear superiority in professional work benchmarks, such as GDPval and enterprise tool-calling evaluations. Here, OpenAI’s focus on real-world professional and economic use cases becomes evident, where the practical value of outputs matters more than outperforming abstract academic benchmarks.

  • GPT-5.2 vs. Claude Opus 4.5

Claude Opus 4.5, released in late November, followed a different path. Anthropic focused on what it calls “hybrid reasoning,” combining extended reasoning with stronger core intelligence. The result is a model that excels in software engineering and open-ended tasks. On the SWE-bench Verified benchmark, Opus 4.5 scored 80.9%, narrowly surpassing GPT-5.2, which achieved 80%.The real distinction between the two models lies in their working style. Opus 4.5 tends to produce longer, more reflective responses, while GPT-5.2 Thinking emphasizes tool usage and the generation of structured outputs such as spreadsheets and presentations. Although the two models are closely matched in agentic programming tasks and complex code refactoring, OpenAI is betting that GPT-5.2 holds a clear advantage in enterprise workflows that rely on reports, presentations, and formal documentation.This simplified comparison shows that the competition is no longer about which model is universally the strongest, but rather about which model best serves a specific type of work—and within which professional or analytical context.

What Is the Impact of GPT-5.2 on Big Data Analytics?

The global big data analytics market was valued at USD 307.52 billion in 2023, and is expected to grow from USD 348.21 billion in 2024 to USD 961.89 billion by 2032, at a compound annual growth rate (CAGR) of 13.5%. Within this rapidly expanding context, the launch of GPT-5.2 should not be seen as an isolated technical upgrade, but rather as an accelerating force that is reshaping how large-scale data is handled. The most direct impacts of GPT-5.2 on big data analytics and decision-makers can be summarized as follows:
  • Accelerating insight extraction from unstructured data: The model’s ability to understand long contexts and process massive volumes of text and documents enables analysts to convert huge amounts of unstructured data into actionable knowledge in significantly less time.
  • Enhancing the efficiency of complex cognitive analysis: Thanks to step-by-step reasoning capabilities, it has become possible to analyze multi-variable relationships and connect economic and behavioral patterns at scale with accuracy approaching expert levels.
  • Improving decision quality in high-risk environments: Lower error rates and increased output reliability make GPT-5.2 a strong decision-support tool when working with large datasets that influence major organizational strategies.
  • Strengthening intelligent automation of analytical workflows: The model supports multi-step processes—from data collection and cleansing, through analysis, to report generation—within a more integrated and cohesive system.
  • Enabling analysis through enterprise business tools: GPT-5.2’s focus on working with spreadsheets, presentations, and enterprise tools makes it directly applicable to business intelligence and big data analytics environments within organizations.
What this shift reveals is that true value does not lie in the tool alone, but in the mind capable of directing it and critically evaluating its outputs. As models like GPT-5.2 become more powerful, expectations of data analysts themselves continue to rise.Here, the role of the Data Analysis & Business Intelligence Diploma from  IMP offered by the Institute of Management Professionals (IMP) becomes clear. It serves as a structured training pathway that prepares learners to work effectively with this new generation of analytics by enabling them to:
  • Understand the full big data analytics lifecycle, from data sources to decision-making.
  • Build a solid foundation in data cleansing, modeling, and analysis.
  • Master business intelligence tools, automation, and analytical visualization.
  • Develop the ability to interpret results and connect them to business context through data storytelling.
  • Cultivate analytical thinking that evaluates AI outputs rather than passively consuming them.
Those who possess the right methodology and deep analytical skills are best positioned to transform this technological power into real value. This is precisely what the Data Analytics and Business Intelligence Diploma from IMP aims to build—step by step.One message may be the difference between merely observing transformations and actively shaping them. If you are seeking to develop your skills, enhance your team’s capabilities, and consciously leverage the power of artificial intelligence in data analytics and decision-making, connecting with the Institute of Management Professionals is the first step on this professional path.