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GPT-5.2 (OpenAI)

GPT-5.2 is OpenAI's December 2025 flagship release, positioned by the company as a step forward in agentic AI workflows where models plan, use tools, and carry out multi-step tasks on behalf of users. Reporting around launch framed it as OpenAI's latest move in the competitive agentic AI race, with OpenAI claiming the model hallucinates less and is more accurate than its predecessor. That emphasis on reliability and grounded reasoning is the central story of the release, signaling OpenAI's focus on practical, task-completing behavior rather than purely conversational polish.

The most concrete measure of GPT-5.2's capabilities comes from abstract-reasoning benchmarks, where it reportedly achieved roughly 53–54% on ARC-AGI-2, a substantial leap over earlier OpenAI models that scored near zero on the same test. That result positioned GPT-5.2 as a meaningful advance in tackling novel, pattern-based problems that resist memorization, a useful proxy for the kind of adaptive thinking agentic applications demand. In practice, GPT-5.2 fits workflows that benefit from deeper reasoning over long, multimodal inputs and structured tool use, while the broader competitive landscape continues to evolve quickly around it.

LLM Gatewayopenai/gpt-5.2gpt

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LLM Gateway
Model key
openai/gpt-5.2
Release date
Dec 11, 2025
Last updated
Dec 11, 2025
Knowledge cutoff
2025-08-31
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$1.75
Output token cost
$14.00

Limits

Input tokens
272,000 tokens
Output tokens
128,000 tokens
Context window
400,000 tokens

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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CoverageBenchmark

An independent arXiv preprint by Tufino, Giovanzana, Zamboni, Onorato, and Oss (University of Modena and Reggio Emilia / University of Trento, 2026) benchmarks GPT-5.2 alongside Gemini 3 Pro and Gemini 3 Flash on the Classical Relativity Concept Inventory (CRCI), a 21-item instrument on Galilean relativity that was not Qualitative coding along visual interpretation, physics reasoning, and coordination dimensions revealed that all three models fail completely on a small subset of items, with errors stemming predominantly from misinterpretations of visual content rather than deficits in physics knowledge. The paper also highlights a st

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