OpenAI has introduced GPT-5.2, a major step forward in its lineup of advanced models designed for serious work, long projects, and always-on agents. It comes in three versions: Instant, Thinking, and Pro. Inside ChatGPT, you’ll see them as ChatGPT-5.2 Instant, ChatGPT-5.2 Thinking, and ChatGPT-5.2 Pro.
In the API, the equivalents are gpt-5.2-chat-latest, gpt-5.2, and gpt-5.2-pro. Each version serves a different purpose:
- Instant is built for quick help, daily tasks, and learning.
- Thinking is meant for deeper, multi-step work and long-running agent workflows.
- Pro offers the most power, aimed at tough technical challenges, detailed analysis, and heavy workloads.
How do these upgrades translate into real results? The details are coming up next. ↓
A Noticeable Jump in Financial Modeling Accuracy
Performance Across Real-World Jobs
GPT-5.2 Thinking is meant to be the primary engine of working in the real world. It has a higher percentage than the best performers of 70.9 percent of 44 occupations considered on GDPval, a broad evaluation that compares it to top professionals of 9 major industries.
Better still, it has been doing so at over 11 times speed and less than 1 percent of the average expert price.
In the case of teams, it is reassuring because you can count on it to deliver organized work like presentations, spreadsheets, schedules, and diagrams with clear and step-by-step instructions.
Stronger Results in Financial Modeling
Internal testing shows meaningful gains for spreadsheet-heavy tasks used in junior investment banking roles. Scores increased from 59.1% with GPT-5.1 to 68.4% with GPT-5.2 Thinking, and up to 71.7% with GPT-5.2 Pro.
These tasks include three-statement models, LBO models, and spreadsheets that must follow strict formatting, structure, and citation rules, the kind of work that appears in many enterprise workflows. The improvements suggest better accuracy, better consistency, and smoother handling of multi-layered financial logic.
Advancements in Software Engineering Benchmarks
GPT-5.2 Thinking has 55.6% on SWE-Bench Pro and 80.0% on SWE-Bench Verified in software development. SWE- Bench Pro is a metric on the capacity to produce patches on a repository scale and in various languages, whereas SWE- Bench Verified is limited to Python.
These scores represent a definite improvement in working with actual codebases, interpretation of the context when dealing with large repositories, and the provision of fixes that fit directly into an existing project.
Built for Long Context and Deep Workflows
Stronger Long-Context Performance
Long context was a major focus for this release, and GPT-5.2 Thinking shows clear progress.It establishes a new performance level on OpenAI MRCRv2 benchmark, which is a test of whether a model can locate multiple identical queries (the needle) hidden within large conversation haystacks.
It was initially the first model to be reported to reach close to perfect accuracy on the 4-needle test up to 256k tokens.
Key Points
- Designed to support very long conversations, documents and workflows.
- Almost 100% on multi-needle retrieval tests.
- Stable even with hundreds of thousands of token-wide input.
When workloads exceed that limit, GPT-5.2 Thinking is linked to the Responses/compact endpoint. This is similar to the compression and reorganization of context to stretch the usable window and is helpful in long, heavy tool job steps that may be run step by step.
Why This Matters
- Applicable in developing agents that last hours or days.
- Keeps state between most tool calls.
- Avoids context loss in high-level, multi-stage workflows.
Better Tool Use and More Reliable Agent Workflows
The GPT-5.2 Thinking also works well to coordinate tools in multi-turn tasks. It scores 98.7% on Tau2-bench Telecom, a benchmark that replicates a customer support scenario in which a model must operate various tools in order.
Highlights
- Manages complex tasks better.
- Make tool calls in the correct order.
- Pulls back on branching workflows and edge cases.
OpenAI’s examples show how it deals with a traveler facing a delayed flight, missed connection, lost bag, and a medical seating requirement. GPT-5.2 manages to:
- Rebook the flight
- Secure special-assistance seating
- Track and update the lost bag
- Handle compensation steps
Meanwhile, earlier versions like GPT-5.1 often left some steps incomplete.
A Step Forward in Charts, Interfaces, and Complex Math
Sharper Visual Understanding Across Real Tasks
GPT-5.2 Thinking shows noticeable improvements in how it handles visual information. With Python tools enabled, it cuts error rates nearly in half on chart-based reasoning and interface interpretation tests such as CharXiv Reasoning and ScreenSpot Pro.
Its spatial awareness is also better. When asked to label motherboard components with rough bounding boxes, GPT-5.2 identifies more regions with tighter, more accurate placement than GPT-5.1.
Higher Accuracy in Scientific and Mathematical Workloads
GPT-5.2 also advances its performance in scientific and mathematical tasks. GPT-5.2 Pro reaches 93.2% and GPT-5.2 Thinking reaches 92.4% on GPA Diamond, a benchmark that covers graduate-level physics, chemistry, biology, and advanced mathematics.
For mathematical problem-solving, GPT-5.2 Thinking solves 40.3% of FrontierMath Tier 1–3 questions when paired with Python tools. OpenAI also notes early examples where GPT-5.2 Pro contributed to a proof in statistical learning theory, with human experts verifying the steps.
From GPT-5.1 to 5.2: What’s New
| Model | Positioning | Context Window / Max Output | Knowledge Cutoff | Key Benchmark Highlights |
| GPT-5.1 | Designed for coding and agent-like tasks with adjustable reasoning depth | 400,000-token context, 128,000-token max output | 2024-09-30 | SWE-Bench Pro: 50.8% SWE-Bench Verified: 76.3% ARC-AGI-1: 72.8% ARC-AGI-2: 17.6% |
| GPT-5.2 (Thinking) | New flagship for complex work across industries and long-running agent workflows | 400,000-token context, 128,000-token max output | 2025-08-31 | GDPval: wins or ties 70.9% vs industry experts SWE-Bench Pro: 55.6% SWE-Bench Verified: 80.0% ARC-AGI-1: 86.2% ARC-AGI-2: 52.9% |
| GPT-5.2 Pro | Higher-compute version built for the most demanding reasoning and scientific tasks | 400,000-token context, 128,000-token max output | 2025-08-31 | GPQA Diamond: 93.2% (vs 92.4% for GPT-5.2 Thinking and 88.1% for GPT-5.1 Thinking) ARC-AGI-1: 90.5% ARC-AGI-2: 54.2% |
Key Takeaways
GPT-5.2 Thinking is the new workhorse: It is a replacement of GPT-5.1 Thinking as the primary model of coding, knowledge work, and agent-driven workflow. It shares the identical 400k context and 128k max output, yet provides significantly higher performance on benchmarks including GDPval, SWE-Bench, ARC-AGI and scientific QA tasks.
GPT-5.2 Thinking is better than GPT-5.1 Thinking even when token constraints remain constant on large benchmarks. Examples include:
- SWE-Bench Pro: 50.8% – 55.6%
- SWE-Bench Verified: 76.3% – 80.0%
- ARC-AGI-1: 72.8% – 86.2%
- ARC-AGI-2: 17.6% – 52.9%
This higher-compute version excels in the toughest tasks. It achieves 93.2% on GPA Diamond (compared to 92.4% for GPT-5.2 Thinking and 88.1% for GPT-5.1 Thinking) and consistently scores higher on ARC-AGI benchmarks, making it ideal for high-end scientific and analytical work.
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FAQs
GPT-5.2 improves accuracy, handles longer context windows, performs better on coding and analytical tasks, and includes a Pro version for high-end reasoning and scientific work.
There are three main variants: Instant (for everyday use), Thinking (for complex, multi-step tasks), and Pro (higher compute for challenging technical and analytical problems).
Yes, GPT-5.2 is designed for long context work. It can manage tasks spanning hundreds of thousands of tokens and maintain coherence over long inputs.
GPT-5.2 Thinking beats or ties top industry professionals in 70.9% of tasks on GDPval and shows significant improvements in coding, investment modeling, and scientific tasks over GPT-5.1.
