GPT-5.2 Pro
AssessTools
OpenAI's advanced AI model used to assist derivation and verification.
Why it's here
Placed in Assess: 6 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 46%.
Evidence (6)
- 6OpenAI Blog·3/4/2026researchGPT-5.2 Pro Helps Extend Graviton Amplitude Research
A new preprint extends single-minus amplitudes to gravitons, reporting nonzero graviton tree amplitudes in quantum gravity. GPT-5.2 Pro was used to help derive and verify the results, highlighting an AI-assisted workflow in theoretical physics research.
- 8OpenAI Blog·2/13/2026breakthroughGPT-5.2 Finds a New Result in Theoretical Physics
A new preprint reports that GPT-5.2 proposed a new formula for a gluon amplitude, which was later formally proved and verified by OpenAI and academic collaborators. The result is notable as a documented instance of a language model contributing to a novel finding in theoretical physics.
- 7OpenAI Blog·2/5/2026model_releaseGPT-5.3-Codex System Card
OpenAI says GPT-5.3-Codex is its most capable agentic coding model so far. It combines GPT-5.2-Codex's coding performance with GPT-5.2's reasoning and professional knowledge capabilities.
- 7OpenAI Blog·1/27/2026product_launchOpenAI Introduces Prism Workspace
OpenAI introduced Prism, a free LaTeX-native workspace with GPT-5.2 built in. The product is designed to help researchers write, collaborate, and reason in a single environment.
- 4OpenAI Blog·1/22/2026product_launchPraktika’s adaptive AI language tutoring with GPT-4.1 and GPT-5.2
Praktika describes how it uses GPT-4.1 and GPT-5.2 to power conversational language tutors that adapt lessons to each learner. The system tracks progress and aims to help users build practical fluency in real-world scenarios.
- 5OpenAI Blog·1/8/2026researchNetomi’s enterprise scaling lessons for agentic AI systems
OpenAI highlights how Netomi scales enterprise AI agents by combining GPT-4.1 and GPT-5.2 with concurrency, governance, and multi-step reasoning. The approach is presented as a way to make agentic workflows more reliable in production settings.