OpenAI just launched GPT-5.2ChatGPT, the API, and its advanced Frontier model are implementing it.
GPT-5.2 family consists of three versions. ChatGPT shows users ChatGPT-5.2 Thinking, Instant and Pro. The API has the models corresponding to the API. gpt-5.2-chat-latest, gpt-5.2Then, gpt-5.2-pro. Think targets agents and complex work, while Pro is more focused on technical tasks and analysis.
Benchmark Profile: From GDPval Bench to SWE Bench
GPT-5.2 is the primary tool for knowledge work in real life. The model is able to produce outputs with more than 11x the speed of an expert and at less than 1% of their estimated cost. The model will reliably create artifacts for engineering teams, such as spreadsheets, diagrams, presentations and schedules, given structured input.
The average score on an internal benchmark for junior investment bank spreadsheet modeling tasks rose from 59.1 % with GPT 5.1 to 68.4 % with GPT 5.2 Thinking, and 71.7 % with GPT 5.2 Pro. Three statement models as well as leveraged purchase models are included in these tasks, with formatting and citations constraints. These workflows represent many of the structured enterprise workflows.
GPT-5.2 thinking scores 55.6 percent in SWE Bench Pro, and 80 percent for SWE Bench Verified. SWE Bench Pro assesses patch generation at the repository level across multiple languages. SWE Bench verified is focused on Python.
Agentic and long context workflows
Designing for long contexts is an important goal. GPT-5.2 Thinking sets a new state of the art on OpenAI MRCRv2, a benchmark that inserts multiple identical ‘needle’ queries into long dialogue “haystacks” The model is tested to see if it can accurately reproduce the answer. The model is reported as the first to have near-100 percent accuracy for the 4 needle MRCR out to 256k tokens.
GPT-5.2 Thinks in response to workloads exceeding that context /compact This endpoint compresses the context to expand the time window available for jobs that are long and require a lot of tools. This can be useful if building agents which iteratively calls tools and maintains state over the raw token limits.
GPT-5.2 Thinkin reaches 98% on Tau2-bench Telecom. This is a benchmark that measures tool use in a real workflow. OpenAI’s official release examples show scenarios such as a passenger with a late flight, missed connecting flight, lost bag, and a medical seat requirement. GPT 5.2 handles rebooking, compensation, and special assistance seating in an orderly manner, while GPT 5.1 does not.
Science and mathematics of vision
The quality of vision also improves. When a Python-based tool is used, GPT 5.2 Thinking reduces error rates in benchmarks such as CharXiv reasoning and ScreenSpot Pro. GPT-5.2.1 shows an improved understanding of spatial images. When labeling components on motherboards with bounding boxes that approximate the actual boundaries, GPT-5.2.1 identifies regions in a more precise placement.
GPT 5.2 thinking solves 40.3% (of FrontierMath Level 1 to Tier 3) problems using Python with GPT 5.2 pro scoring 93.2 on GPQA Diamond. The benchmarks include graduate-level physics, biology, chemistry and mathematics. OpenAI also highlights an early application where GPT 5.2 Pro was used to prove statistical learning theory with human verification.
Comparative Table
| Model | Primary positioning | Max output / Context Window | The Knowledge Cutoff | Noteworthy benchmarks: Thinking / Pro and GPT-5.1 thinking |
|---|---|---|---|---|
| GPT-5.1 | Flagship model with configurable reasoning for agents and coding. | 400,000 tokens context, 128,000 max output | 2024-09-30 | SWE-Bench Pro 50.8 percent, SWE-bench Verified 76.3 percent, ARC-AGI-1 72.8 percent, ARC-AGI-2 17.6 percent |
| GPT-5.2 (Thinking) | A new model that can be used for all industries to perform coding and agentsic tasks, as well as for the long-running agent | 400,000 tokens context, 128,000 max output | 2025-08-31 | The GDPval score is 70.9 percent or higher compared to industry professionals. SWE Bench Pro scores 55.6 percent. SWE Bench Verified Scores 80.0 percent. ARC AGI-1 86.2 percentage. ARC AGI-2 52.9 per cent. |
| GPT-5.2 | GPT-5.2 Higher Compute Version for Scientific and Reasoning Workloads produces more intelligent and precise answers | 400,000 tokens context, 128,000 max output | 2025-08-31 | GPQA Diamond 93,2 percent vs. 92,4 percent for the GPT-5.2 thinking and 88.1 for the GPT 5.1 thinking. ARC-AGI-1 is 90.5 percent whereas ARC-2 is 54.2 percent. |
What you need to know
- The new standard workhorse is GPT-5.2.The GPT-5.1 model is replaced as the primary one for agents, knowledge and coding. However, the context remains the same at 400k and there are no changes to the maximum output.
- Significant accuracy improvement over GPT-5.1 on a similar scaleGPT-5.2 thinking moves key benchmarks from 50,8 percent to 55,6 percent for SWE Bench Pro, from 76,3 percent to 80,0 percent for SWE Bench Verified and from 72,8 percent to 86,2 percent for ARC AGI-1, from 17,6 percent up to 52,9 percent in ARC AGI-2 while maintaining token limits.
- GPT-5.2 pro is a high end science and reasoning tool.GPT 5.2 Pro has a more powerful computer that improves reasoning, scientific and hard tasks. For example, GPQA Diamond scores are 93.2 percent compared to 92.4 percent in GPT 5.2 Thinking. GPT 5.1 Thinking is 88.1 percent. Scores on ARC AGI tiers also increase.
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