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Model details

OpenAI GPT-5.2 Pro

GPT-5.2 pro represents a high-capability tier within the GPT-pro family, built to handle complex, multi-turn interactions that demand both depth and reliability. The model is positioned as a professional-grade solution for demanding workloads, supporting not just standard text generation but also vision capabilities for image inputs, structured outputs for programmatic use, and native agent orchestration for automated task execution. Its architecture is designed to balance power with control, making it suitable for applications ranging from sophisticated coding assistants to retrieval-augmented knowledge systems that reference multiple external passages in a single response.

The training lineage builds on OpenAI's extensive post-training methods, including refined alignment techniques and extensive capability cultivation that prepare models for real-world deployment scenarios. Source documentation highlights the model's suitability for production traffic sizing, multi-turn pair programming exchanges, and scenarios requiring extended context handling beyond simpler chat workflows. The model integrates tool-use and function-calling capabilities natively, allowing developers to extend functionality without complex custom integrations. As part of a broader family that includes capabilities like reasoning and attachments, GPT-5.2 pro is engineered for teams and enterprises that need a versatile, capable model for sophisticated automation and analysis tasks.

DigitalOceanopenai-gpt-5.2-progpt-pro

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Provider
DigitalOcean
Model key
openai-gpt-5.2-pro
Release date
Dec 11, 2025
Last updated
Dec 11, 2025
Knowledge cutoff
2025-08-31
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$21.00
Output token cost
$168.00

Limits

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

Transparent token rates

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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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Latest news about OpenAI GPT-5.2 Pro

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CoverageBenchmark

OpenAI's GeneBench paper introduces a benchmark of 103 multi-stage inference problems across genomics and quantitative biology, designed so that realistic obstacles such as measurement error, selection bias, confounding, QC failures, and competing model classes must each be addressed through dependent inferential forks Even for the two strongest reported Pro-harness configurations (GPT-5.5 and GPT-5.4 Pro), 60.2% and 62.1% of problems respectively remain below a 20% pass rate over repeated runs, underscoring that GeneBench is intended as a hard, decision-chain stress test rather than a saturated leaderboard. GPT-5.2 Pro's 10.8% figur

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