GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning,...
OpenAI GPT-5.2 Pro is a large language model built for tasks that demand both breadth and depth. With a context window of 400,000 tokens and a maximum output of 128,000 tokens, it can ingest and…
GPT-5.2 Pro excels at tasks that require understanding and reasoning over large amounts of information. Examples include analyzing entire research papers or legal documents, debugging and refactoring large codebases, and generating detailed reports or plans that span many pages. Its multimodal input allows it to interpret images and files in the same context as text, so it can, for instance, transcribe diagrams from a PDF while answering questions about the text. The model's high output capacity enables it to produce long-form content without needing multiple API calls, reducing complexity. These capabilities make it a strong candidate for research, engineering, and analytics roles where depth and completeness are critical.
Because GPT-5.2 Pro is priced at $21.00 per 1M input tokens and $168.00 per 1M output tokens, it is expensive for high-volume or latency-sensitive applications. For tasks that fit within a smaller context window (e.g., 8k–32k tokens) and do not require multimodal input, cheaper models like OpenAI's GPT-4o or GPT-3.5 series can provide adequate quality at a fraction of the cost. Also, if the output length needed is under a few thousand tokens, a smaller model will be far more economical. Users are advised to match model capability to task complexity: use GPT-5.2 Pro only when the problem genuinely demands its large context, multimodal support, or high output ceiling. OrcaRouter's catalog includes many models to choose from.
The model accepts files and images as part of the input messages. For images, you can provide a URL or base64-encoded image in the content array with "type": "image_url". For files, OrcaRouter's API supports file attachments; the file is processed and its contents are added to the token stream. The entire input—text, images, files—must fit within the 400,000-token context window. Note that images and files consume tokens based on their size; detailed images may consume thousands of tokens. This allows the model to read text from images (OCR-like) and reason across multiple formats simultaneously. However, because of the token consumption, be mindful of the cost when including large attachments.
Specific benchmark scores for GPT-5.2 Pro are not provided in the available facts. Performance can be inferred from the model's architecture as a high-end offering in OpenAI's GPT-5 lineup, which generally demonstrates strong results on reasoning, coding, and multimodal benchmarks. However, without published figures, users should evaluate the model on their own test sets. OrcaRouter does not alter model performance; you call the same model hosted by OpenAI. For critical applications, run controlled experiments comparing GPT-5.2 Pro against other models. Typical strengths include deep comprehension of long contexts and high accuracy on complex tasks, but actual scores depend on the specific prompt and domain.
Latency for GPT-5.2 Pro is not specified in the provided facts. As a large model with a 400k context and 128k output, it is likely slower than smaller models, especially for requests that utilize the full context window. Processing time increases with input size and requested output length. Under OrcaRouter's API, network latency and OrcaRouter's infrastructure add minimal overhead, but the dominant factor is OpenAI's inference time. For real-time applications, consider models with faster response characteristics. For offline batch processing, the slower speed may be acceptable given the quality gains. Always measure latency in your environment, as it can vary with load and request parameters.
Strengths: Very large context window (400k tokens) and output limit (128k tokens) enable analysis of extensive materials in one pass. Multimodal input (image, text, file) allows diverse data sources to be combined. The model is designed for high-quality reasoning on complex tasks. Limitations: High cost per token makes it uneconomical for simple or short queries. No benchmark scores are publicly listed, so relative performance on standard tasks is unknown. Output quality may degrade when the context is filled with excessive irrelevant information. As with all large models, it can produce plausible but incorrect responses. Users should validate outputs, especially in critical domains. The model does not support real-time streaming speeds; it is optimized for depth over speed.
Pricing is $21.00 per 1 million input tokens and $168.00 per 1 million output tokens. These are the provider rates with zero markup from OrcaRouter. Input tokens include all text, image tokens (based on resolution), and file tokens. Output tokens are the generated response text. There are no additional fees for API calls, authentication, or support. You are billed only for the tokens consumed. Caching is not mentioned; assume no token caching is applied unless OrcaRouter's documentation specifies otherwise. To estimate cost: a 10,000-token input and 5,000-token output would cost (10,000/1,000,000)*21 + (5,000/1,000,000)*168 = $0.21 + $0.84 = $1.05. For longer requests, costs scale linearly.
The input cost of $21/1M tokens is moderate among high-end models, but the output cost of $168/1M tokens is significantly higher. This means you want to minimize output length when possible. For a 128k-token output, the cost alone would be about $21.50 just for generation. If your task can be split into smaller pieces with a cheaper model, you may save money. However, for tasks that truly require the large context or multimodal capability, the per-call expense may be justified. Always set max_tokens to the minimum needed. Consider using OrcaRouter's cost tracking features to monitor spend. There is no discount for batch or burst usage stated; rates are per-token regardless of volume.
No. OrcaRouter bills GPT-5.2 Pro at the exact provider rate: $21.00 per 1M input tokens and $168.00 per 1M output tokens, with zero markup. There are no hidden fees, monthly subscription charges, or per-request surcharges. You pay only for the tokens consumed. This transparent pricing allows you to compare costs directly with OpenAI's own API pricing if you were to access it directly. OrcaRouter's role is as a gateway: it forwards your requests to the upstream provider and returns the response, without altering the model or adding its own pricing layer. All standard billing for API usage applies.
Use the base URL https://api.orcarouter.ai/v1 with your API key. Set the model parameter to "openai/gpt-5.2-pro". The request format is identical to OpenAI's Chat Completions API (POST /chat/completions). Include a messages array with your conversation history. For image input, include a message with content containing "type": "image_url". For file input, use the file attachment mechanism—consult OrcaRouter's documentation for exact syntax. You can set standard parameters like temperature, top_p, max_tokens (up to 128,000), and stop sequences. The response will contain the generated text in the same format as OpenAI's API. Example (Python): openai.ChatCompletion.create(model="openai/gpt-5.2-pro", messages=[...]).
All standard Chat Completions parameters apply: messages (required), model (required, set to "openai/gpt-5.2-pro"), max_tokens (default none? set as needed up to 128000), temperature (0-2, default 1), top_p (0-1, default 1), n (number of completions, default 1), stream (boolean, default false), stop (array of strings), presence_penalty and frequency_penalty (-2 to 2). For multimodal, include in the message content array: text objects (type "text") and image_url objects (type "image_url" with url field). Files are passed via a separate parameter; check OrcaRouter docs. The token limits are enforced by the provider. There is no custom parameter for context window size; the model automatically uses up to 400k total tokens. The API returns the completion in standard format, including usage statistics.
Migration is straightforward: change the base URL from api.openai.com to https://api.orcarouter.ai/v1, and replace the model ID from any OpenAI model name to "openai/gpt-5.2-pro". Replace your API key with an OrcaRouter key. The message format remains identical—no changes to how you structure multimodal inputs, parameters, or streaming. If you were using OpenAI's Python library, switch the api_base to OrcaRouter's URL. Note that OrcaRouter does not add any latency overhead compared to direct access. Because pricing is same as provider rate, your costs will be identical. Test with a small request first to confirm connectivity and token counts. All standard error codes and usage fields are preserved.
GPT-5.2 Pro has a much larger context window (400k vs. typically 128k for GPT-4 Turbo or 128k for GPT-4o) and a higher output limit (128k vs. typically 4k–16k for earlier models). It also supports image and file inputs, which GPT-4o also does, but with a smaller context. Cost-wise, GPT-5.2 Pro is significantly more expensive: $21/$168 vs. approximately $2.50/$10 for GPT-4o (per 1M tokens). The trade-off is that GPT-5.2 Pro can handle much larger tasks in a single pass, reducing complexity and potential context fragmentation. For short tasks, GPT-4o offers comparable quality at lower cost. For deep analysis of large documents or multimodal-heavy tasks, GPT-5.2 Pro is the more powerful choice.
Without specific benchmark scores, a direct performance comparison is not possible. Both models offer large context windows—Gemini 1.5 Pro supports up to 2M tokens, while GPT-5.2 Pro supports 400k. GPT-5.2 Pro has a higher output limit (128k) compared to Gemini 1.5 Pro's typical 8k–32k. Input modalities are similar: both accept text, image, and files. Pricing differs: Gemini 1.5 Pro's pricing varies by input size. GPT-5.2 Pro's pricing is fixed per token. The choice depends on required output length, cost tolerance, and specific model strengths. OrcaRouter provides access to both, so users can evaluate on their own data to determine which yields better results for their use case.
Choose GPT-5.2 Pro when your task requires any of: a context window larger than 128k tokens (e.g., processing entire books, long conversation logs, massive codebases), generated output longer than 16k tokens (e.g., full reports, extensive code generations), or high reliability on complex reasoning that demands the extra capability of a top-tier model. Also choose it if you need multimodal input with very large context—other OpenAI multimodal models have smaller limits. Avoid it for simple Q&A, short translations, or small-scale data extraction, where cheaper models like GPT-4o mini or GPT-3.5 Turbo deliver good results at a fraction of the cost. Use OrcaRouter to switch models easily based on each request's requirements.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.2-pro",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $21.00 |
| Output / 1M tokens | $168.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/openai/gpt-5.2-proOpen @misc{orcarouter_gpt_5_2_pro,
title = {GPT-5.2 Pro API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.2-pro}
}OpenAI. (2025). GPT-5.2 Pro API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.2-pro