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GPT-5.2 Pro (OpenAI)

GPT-5.2 Pro is OpenAI's premium reasoning model in the gpt-pro family, positioned as a multimodal system aimed at coding, software engineering, and agent workflows where accuracy and control matter. Independent listings describe it as combining large-context reasoning with vision input and tool use, suiting tasks that require deep step-by-step thinking over long inputs. The model is publicly available with active pricing and is explicitly framed around developer-facing and agentic use cases rather than casual chat or content generation.

On the technical side, the model operates with a 400,000-token context window and uses the tiktoken-o200k tokenizer, with a maximum reply length of 128,000 tokens. Public quality benchmarks reported by third-party trackers include MMLU at 87.4, GPQA Diamond at 90.3, and FrontierMath Tier 4 at 31.3, indicating strong general knowledge and graduate-level reasoning alongside a still-difficult frontier math score. Pricing reflects its premium tier at $21.00 per million input tokens and $168.00 per million output tokens, with discounted cache and batch rates available, making it best suited for teams that need reliable long-context reasoning on high-stakes engineering and agent tasks rather than high-volume, low-cost generation.

LLM Gatewayopenai/gpt-5.2-progpt-pro

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LLM Gateway
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

Input tokens
272,000 tokens
Output tokens
272,000 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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CoverageBenchmark

BenchLM publishes a model record for GPT-5.2 Pro listing it as a proprietary reasoning model released on December 11, 2025, with a 400K-token context window. As of August 31, 2026 the page reports API pricing of $21 per million input tokens and $168 per million output tokens, yielding a blended figure of $94.50 per mil Agentic, coding, knowledge, and multilingual performance categories are explicitly marked as not measured, and independent runtime speed (tokens per second and time to first token) has not been measured on the page. The BenchLM profile is therefore useful as a current third-party pricing and positioning snapshot for GP

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