OpenAI's flagship model with 400k token context, multimodal input, and 272k token output, accessed via OrcaRouter at provider rate.
This model is a specific snapshot of OpenAI's GPT‑5 Pro, optimized for complex reasoning and long‑context tasks. It accepts image, text, and file inputs, processes up to 400,000 tokens of context,…
The model accepts three input types: text, images, and files. Text can be standard UTF‑8 strings. Images can be provided as URLs or base64‑encoded data, and the model can analyse visual content such as photographs, diagrams, and screenshots. File inputs cover a range of formats (e.g., PDF, Word, Excel, plain text) allowing the model to read and reason over structured or unstructured documents. All modalities can be combined in a single prompt, enabling tasks like extracting information from a photo of a chart and comparing it with a textual report.
The large context window is ideal for tasks that require maintaining coherence over very long sequences. Examples include analysing an entire research paper or legal brief in one pass, performing multi‑step reasoning over a long conversation history, or processing a complete codebase for refactoring suggestions. It also supports prompt engineering strategies like using a lengthy instruction set or few‑shot examples without truncation. For tasks that naturally fit within shorter contexts, smaller and cheaper models may be more cost‑effective.
While GPT‑5 Pro provides the highest capability, its cost ($15/$120 per million tokens) is significantly higher than models like GPT‑4o mini or open source alternatives. If your task fits within 8k–32k tokens and does not require multimodal input, a smaller model can deliver acceptable results at a fraction of the cost. Also, if output length is small, the per‑token cost dominates. Evaluate whether the 400k context and 272k output are genuinely needed; otherwise, consider lower‑cost options on OrcaRouter.
File inputs are processed according to OpenAI's file handling pipeline. The model can read text from supported file formats and interpret embedded images or tables. For very large files, the token count of the extracted content counts toward the context window. It is recommended to pre‑process files to extract only the relevant sections if token usage is a concern. The OrcaRouter API accepts files through the same parameter structure as OpenAI's API, typically via a file URL or base64 encoding.
No specific benchmark numbers are provided in the listing for this snapshot. As an OpenAI flagship model, it is expected to achieve state‑of‑the‑art results across common NLP, reasoning, and multimodal benchmarks compared to earlier GPT models. Users should evaluate it on their own representative tasks to confirm suitability. The large context window does not degrade performance on short inputs; the model retains its reasoning strength regardless of context length.
Latency is generally higher than smaller models due to the large context and output capacity. Time‑to‑first‑token and overall generation speed depend on prompt length, output length, and current OpenAI server load. For tasks requiring only short responses, faster models like GPT‑4o mini may be more responsive. OrcaRouter does not introduce significant latency beyond the provider's response time. No specific latency numbers are available for this snapshot.
Its strengths include deep multi‑step reasoning, handling very long contexts with minimal information loss, multimodal understanding (combining text, images, files), and generating structured, coherent outputs up to 272k tokens. It excels at tasks that involve complex instructions, large datasets, or require synthesising information from multiple sources within a single prompt. For example, it can analyse a 300‑page document and produce a comprehensive summary with citations.
Despite its power, the model is not infallible. It may still produce hallucinations, especially on obscure topics or when the context contains contradictory information. The large context window does not eliminate errors in attention; long inputs can sometimes cause the model to miss subtle details. Also, the high output token limit can lead to very long but not necessarily all‑accurate responses. Users should implement verification for high‑stakes outputs. Pricing is a limitation for budget‑sensitive applications.
Input tokens are billed at $15.00 per 1 million tokens, and output tokens at $120.00 per 1 million tokens. These are the exact rates set by OpenAI; OrcaRouter adds zero markup. For example, a prompt consuming 50,000 input tokens and generating 10,000 output tokens would cost $0.75 for input and $1.20 for output, totalling $1.95. No additional fees apply beyond the per‑token consumption.
The 8× ratio between input and output pricing reflects the greater computational cost of generating tokens. Generating lengthy, coherent responses requires more processing than encoding input. The high output price also encourages efficient prompting: for tasks that can be accomplished with short answers, using a lower‑output model may be more economical. With OrcaRouter's zero‑markup policy, you pay exactly the provider's cost for each token.
OrcaRouter does not offer its own token caching or discount programs for this model; you are billed per token at the provider rate. Some providers may offer lower rates for batch processing or reserved capacity, but this snapshot is available only as an on‑demand API call. If you have very high volume, contact OrcaRouter support to discuss potential custom arrangements, though no specific discounts are advertised.
Estimate by computing the number of tokens in your average prompt and the expected output length. Use OpenAI's tokeniser or the OrcaRouter API's token‑counting endpoint. For example, a 100,000‑token prompt with a 50,000‑token response costs $1.50 for input + $6.00 for output = $7.50 per call. If your application makes thousands of such calls, costs can quickly escalate. Consider using smaller models for simpler tasks and reserve GPT‑5 Pro for the most demanding ones.
Use the OrcaRouter base URL https://api.orcarouter.ai/v1 with the model ID "openai/gpt-5-pro-2025-10-06". The API is fully compatible with OpenAI's chat completions endpoint. A typical request includes your API key, model parameter, messages array, and optional parameters such as max_tokens, temperature, and top_p. For multimodal inputs, include image_url or file_url fields within the message content. Responses are returned in the same format as the OpenAI API.
The max_tokens parameter can be set up to 272,000 (subject to the context window limit). The temperature range is 0–2, with lower values producing more deterministic outputs. top_p (0–1) controls nucleus sampling. frequency_penalty and presence_penalty range from –2 to 2. For multimodal inputs, you must specify a content type (text, image_url, file_url). The stop parameter accepts up to four sequences. All other parameters supported by OpenAI's chat completions are also supported.
Change the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1, and replace the API key with your OrcaRouter API key. Use the model name "openai/gpt-5-pro-2025-10-06" exactly. All other request and response structures remain identical. No retraining or code changes beyond the endpoint and key are required. OrcaRouter supports the same streaming and non‑streaming modes. Test with a low‑cost query first to verify billing and functionality.
Authentication uses an API key sent in the Authorization header (Bearer token). OrcaRouter's rate limits may differ from OpenAI's direct limits; check your OrcaRouter account dashboard for details. For high‑volume usage, consider batching requests or contacting support for limit increases. The model itself inherits OpenAI's rate limits on the backend. Ensure your application handles rate limit errors (HTTP 429) with retry logic.
GPT‑4 Turbo (typically a 128k context window and 16k max output) is less expensive and faster for standard tasks. GPT‑5 Pro offers more than triple the context and 17× the output length, enabling tasks that GPT‑4 Turbo cannot handle in a single call. However, GPT‑4 Turbo's lower cost ($10/$30 per 1M tokens) makes it more economical for shorter prompts. Choose GPT‑5 Pro when you need the extended context or output capabilities; otherwise GPT‑4 Turbo is often sufficient.
GPT‑4o supports multimodal inputs (text, images) with a 128k context and 16k output. It is generally faster and cheaper ($5/$15 per 1M tokens) than GPT‑5 Pro. GPT‑5 Pro's larger context and output capacity make it preferable for deep analysis of very long documents or generating long‑form content. For typical multimodal tasks that fit within GPT‑4o's limits, GPT‑4o offers a better price‑performance ratio. Both models are available through OrcaRouter.
If your use case does not require OpenAI's ecosystem or the specific performance of GPT‑5 Pro, models from Anthropic (Claude 3.5 Sonnet) or Google (Gemini 1.5 Pro) may offer comparable capabilities at different price points or with different data handling policies. For example, Claude 3.5 Sonnet has a 200k context and lower output cost. Evaluate based on latency, per‑token pricing, data residency, and specific benchmarks relevant to your task. OrcaRouter provides access to multiple providers for easy comparison.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="openai/gpt-5-pro-2025-10-06",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $15.00 |
|---|---|
| Output / 1M tokens | $120.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
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GET /api/public/models/openai/gpt-5-pro-2025-10-06Open @misc{orcarouter_gpt_5_pro_2025_10_06,
title = {openai/gpt-5-pro-2025-10-06 API},
author = {openai},
year = {n.d.},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5-pro-2025-10-06}
}openai. (n.d.). openai/gpt-5-pro-2025-10-06 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5-pro-2025-10-06