OpenAI's GPT-5.2 Pro with 400K context window, multimodal (image/text/file), accessed via OrcaRouter at zero markup.
openai/gpt-5.2-pro-2025-12-11 is a model from OpenAI's GPT-5 generation, specifically a Pro variant released in December 2025. It is designed for tasks that require large context windows and…
With a 400,000-token context window, the model can process extremely large inputs in a single prompt. This includes entire books, long research papers, extensive codebases, or multi-hour conversation transcripts. The large context allows the model to maintain coherence across long passages and reference earlier parts of the input without relying on external memory or retrieval systems. It is particularly effective for tasks like summarization of lengthy documents, question answering over large corpora, and generating reports that require synthesizing information from many sources.
The model accepts images and files in addition to text. Images can be provided as URLs or base64-encoded data within the messages array. Files can be attached using the file input modality, which allows the model to read and process the content. This capability enables tasks such as describing images, extracting text from scanned documents, analyzing charts, and combining visual and textual information for reasoning. The multimodal support is integrated into the standard chat completion interface, so a single request can contain text, images, and files together.
Given the high cost ($21 per million input tokens, $168 per million output tokens), this model is not economical for simple or short prompts. For tasks like basic classification, short-answer questions, or simple translations, smaller models such as GPT-4o mini or GPT-3.5 Turbo may provide acceptable results at a fraction of the cost. The Pro variant is best reserved for applications that truly leverage its large context window or multimodal input—such as processing entire documents, analyzing images, or generating long-form content where quality and coherence are critical.
Optimal use cases include long-document analysis (legal contracts, research papers, technical manuals), multimodal reasoning (interpreting diagrams, extracting data from invoices), complex code generation and review across large codebases, and extended conversational agents that need to remember long histories. The large output capacity (128K tokens) also makes it suitable for generating lengthy reports, novels, or detailed analyses in a single response. For shorter tasks, a more cost-effective model is recommended.
OpenAI has not publicly released standardized benchmark scores specifically for the GPT-5.2 Pro 2025-12-11 variant. Based on its positioning as a Pro model, it is expected to perform at the forefront of language understanding, reasoning, and generation tasks. Users should evaluate the model on their own datasets to determine its effectiveness for specific applications. The model's large context window and multimodal support may provide advantages in tasks that require processing long or mixed inputs, but comparative quantitative data is not available at this time.
Latency depends on input size, output length, and current load on OpenAI's infrastructure. Because the model has a large context window and high output capacity, processing very long inputs or generating lengthy responses will take more time than with smaller models. For short prompts, response times are similar to other top-tier models. OrcaRouter does not add significant overhead; latency is primarily determined by the provider. Streaming (enabled via the stream parameter) can reduce perceived latency by delivering tokens as they are generated.
Strengths include a 400K context window, 128K max output tokens, multimodal input (image, text, file), and high-quality language generation. Limitations include high cost, lack of disclosed benchmark scores, and potential latency for very long prompts. The model may not be suitable for real-time applications requiring extremely low latency or for budget-constrained projects. Additionally, as with all large language models, it can produce incorrect or biased outputs, and users should implement appropriate validation and safety measures.
openai/gpt-5.2-pro-2025-12-11 is a larger model, so it typically has higher latency per token compared to smaller models like GPT-4o mini or GPT-3.5 Turbo. For short inputs and outputs, the difference may be noticeable but acceptable. For long inputs, the initial time to process the full prompt (e.g., 400K tokens of context) can be significant. If speed is the primary concern, a smaller model is a better choice. However, for tasks that require the large context or multimodal capabilities, the trade-off in speed may be justified.
Pricing is based on the number of tokens processed. Input tokens cost $21.00 per 1 million tokens. Output tokens cost $168.00 per 1 million tokens. Tokens include both the text and any image or file tokens (images are billed at a higher token rate than text, as determined by OpenAI). There are no fixed monthly fees or additional charges from OrcaRouter; you pay exactly the provider rate with zero markup. All billing is done through OrcaRouter's usage-based system.
For a long document of 100,000 input tokens and a 10,000-token summary, the input cost would be $2.10 and the output cost $1.68, totaling $3.78. For a multimodal request with an image (which may be tokenized into several hundred tokens depending on size), costs scale accordingly. Because output tokens are eight times more expensive than input tokens, tasks that generate long responses are more costly. Users should estimate token usage based on their inputs and desired output length to manage costs.
No hidden fees. OrcaRouter does not charge setup fees, monthly minimums, or additional per-request costs beyond the token-based pricing. You pay exactly $21.00 per million input tokens and $168.00 per million output tokens. There is no markup. However, note that image and file inputs may be tokenized at a different rate by OpenAI; this is not an OrcaRouter fee. Always check the actual token count for multimodal inputs as shown in the API response to accurately predict costs.
OrcaRouter does not publicly disclose the details of any caching mechanisms for this model. Caching policies, if any, would be determined by OpenAI and are not reflected in the zero‑markup pricing structure. Users should assume that every request is billed based on the actual token count of the input and output as reported by the API. For cost optimization, consider batching requests, using shorter prompts, and reusing responses where possible rather than relying on caching.
To use openai/gpt-5.2-pro-2025-12-11, set your API client to point to https://api.orcarouter.ai/v1. Use your OrcaRouter API key for authentication. In the chat completion request, specify the model as "openai/gpt-5.2-pro-2025-12-11". The API is compatible with the OpenAI Python client, JavaScript client, and others. For example, in Python: openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="sk-..."). Then call client.chat.completions.create(model="openai/gpt-5.2-pro-2025-12-11", messages=[...]).
All standard OpenAI chat completion parameters are supported, including messages (system, user, assistant roles), temperature (0-2), top_p, n, stream, stop, max_tokens, presence_penalty, frequency_penalty, logit_bias, and user. For multimodal inputs, the content field can contain text and image_url or file_url objects. The max_tokens parameter can be set up to 128,000. Streaming is enabled by setting stream: true and handling the incoming token chunks. Refer to OpenAI's API documentation for detailed descriptions of each parameter.
Migration involves two changes: update the base URL to https://api.orcarouter.ai/v1 and replace your OpenAI API key with an OrcaRouter API key. The model ID changes from the original OpenAI identifier (e.g., "gpt-5.2-pro-2025-12-11") to "openai/gpt-5.2-pro-2025-12-11" to route through OrcaRouter. No other code changes are required because the API is fully compatible. Ensure your library is up to date. Test with a small request to confirm connectivity and billing.
The base URL for OrcaRouter is https://api.orcarouter.ai/v1. The exact model ID to use in the request is "openai/gpt-5.2-pro-2025-12-11". This model ID is required for OrcaRouter to route the request to the correct provider and model. If you use the wrong ID, the request may fail or be routed to a different model. Always verify the model ID in the OrcaRouter documentation or dashboard.
Compared to GPT-4o, GPT-5.2 Pro offers a substantially larger context window (400K vs 128K for GPT-4o) and higher max output (128K vs 4K for GPT-4o). It also adds file input modality. Pricing is significantly higher: GPT-4o costs $10 per million input tokens and $30 per million output tokens (as of known public pricing), while GPT-5.2 Pro costs $21 and $168 respectively. The Pro variant is therefore best for applications that benefit from the extra context and multimodal file support.
Information about the GPT-5 base model and GPT-5.2 non-Pro variants is not provided in the facts. Generally, Pro versions within a generation offer higher performance, larger context windows, and greater output capacity at a higher price. This model specifically is the Pro version of the GPT-5.2 release from December 2025. Users should consult OpenAI's official documentation for differences between model tiers. OrcaRouter may offer other GPT-5 variants; check the model catalog for availability.
Choose GPT-5.2 Pro when you need to process very long contexts (up to 400K tokens) in a single prompt, generate long-form outputs (up to 128K tokens), or leverage multimodal inputs combining text, images, and files. If your task is shorter or simpler, a cheaper model like GPT-4o or GPT-4o mini will be more cost-effective. For multimodal tasks that do not require the full context window, GPT-4o with vision may suffice. Evaluate your specific requirements for context length, output length, and modality to decide.
Comparisons with non-OpenAI models require provider-specific benchmark data, which is not provided. In general, large context windows are also offered by models from Anthropic (Claude 3.5 Sonnet with 200K) and Google (Gemini 1.5 Pro with 1M). GPT-5.2 Pro’s 400K context sits between these. The pricing and output capacity are unique to OpenAI. When evaluating alternatives, consider factors such as context window, token pricing, ease of integration via OrcaRouter, and performance on your specific tasks.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5.2-pro-2025-12-11",
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-pro-2025-12-11Open @misc{orcarouter_gpt_5_2_pro_2025_12_11,
title = {openai/gpt-5.2-pro-2025-12-11 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5.2-pro-2025-12-11}
}openai. (n.d.). openai/gpt-5.2-pro-2025-12-11 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5.2-pro-2025-12-11