GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and...
GPT-5 Pro is a large language model from OpenAI, made available through OrcaRouter's API. It supports input from text, images, and files, and offers a context window of 400,000 tokens with a maximum…
GPT-5 Pro excels at tasks that require processing very long contexts—for example, summarizing an entire book, analyzing a multi-hundred-page legal document, or generating a comprehensive report from a large set of files. Its multimodal capability also enables use cases like describing a series of images in a single response or answering questions that combine text from a PDF with visual content from a photograph. Because the model can retain up to 400K tokens of context, it avoids the fragmentation that smaller models impose. This is particularly valuable for complex reasoning chains that span many pages, such as scientific research reviews or codebase understanding. However, due to its cost, it is recommended only for applications where these capabilities are essential.
To use image and file inputs, include them in the API request following the same multimodal format used by OpenAI. For images, you can provide a base64-encoded string or a URL. For files, you can upload raw text or structured data as part of the message content. The model can then extract information from both modalities simultaneously. Best practices include keeping image resolution reasonable to avoid excessive token usage (since images consume tokens based on resolution) and ensuring file content is relevant. OrcaRouter supports these inputs via the same endpoint as text-only requests; simply include the appropriate fields. Note that the total token count for images and files counts toward the context window, so plan accordingly.
Use a cheaper model like GPT-4o or GPT-3.5 Turbo when your task does not require the full 400K context window, multimodal input, or the extreme output length. For simple question answering, short content generation, or tasks that fit within a few thousand tokens, the cost savings of a smaller model are substantial. GPT-5 Pro's input cost is $15.00 per 1M tokens, while GPT-4o (for example) is around $2.50 per 1M input tokens. Over a high-volume workflow, the difference can be significant. Additionally, if your application does not need image or file inputs, a text-only model suffices. OrcaRouter allows you to mix models on the fly, so you can route simple queries to cheaper models and escalate only when needed.
GPT-5 Pro's large context window makes it ideal for tasks where you need to maintain coherence across many pages of text. For example, you can feed in an entire novel and ask for a detailed analysis, or load a multi-chapter research paper and request a summary with citations. The model can also perform multi-step reasoning over long contexts, such as tracing arguments across different sections. However, because the context is so large, it is important to structure the input clearly—using separators, numbering, or structured formats—to help the model focus. The large output limit also allows generating long-form content in a single response, such as a 272K-token report or codebase. For best results, use system messages to define the task and provide clear instructions.
No benchmark data is publicly available for GPT-5 Pro at this time. Unlike earlier models that had published scores on datasets like MMLU, HellaSwag, or HumanEval, OpenAI has not released performance numbers for this variant. Users should therefore evaluate the model based on their own use cases rather than relying on external benchmarks. Given the pricing and capacity, it is presumed to be the most capable model in OpenAI's lineup, but without empirical benchmarks, this is an assumption. OrcaRouter provides the model as-is; developers are encouraged to run their own evaluations to determine if it meets their requirements. The lack of public benchmarks does not necessarily indicate poor performance—it may simply reflect the model's release cycle.
The primary strengths of GPT-5 Pro are its exceptionally large context window (400K tokens), high output limit (272K tokens), and support for multimodal inputs (image, text, file). These features enable use cases that are impossible with smaller models. The high pricing suggests that it is optimized for quality and depth, likely delivering more coherent and thorough responses over long sequences. Additionally, because it is from OpenAI, it benefits from continuous improvements in architecture and training data. However, without benchmark scores, it is not possible to quantify its performance relative to other large models. Users should test it on their specific tasks, such as long-document QA or multimodal analysis, to assess its capabilities.
Limitations of GPT-5 Pro include its high cost, which can quickly accumulate for long outputs or frequent calls. The absence of public benchmarks makes it difficult to compare objectively with models like Claude 3.5 Sonnet or Gemini 1.5 Pro. Additionally, the model may have latency proportional to the size of the input and output—processing 400K tokens takes time even on optimized hardware. Another consideration is that very long contexts can introduce recency bias or loss of detail in the middle of the window, a known challenge for all large-context models. Finally, while it supports images and files, the token cost of those inputs can be high. OrcaRouter passes through all model behavior; developers should monitor token usage and latency.
Specific latency figures for GPT-5 Pro are not publicly documented. As a large model with a 400K token context and 272K token output, inference time is expected to be longer than smaller models. In practice, time-to-first-token (TTFT) will increase with context size, and total generation time will depend on the requested output length. On OrcaRouter, the model runs on OpenAI's infrastructure, so latency is similar to what you would experience using OpenAI directly. For applications that require real-time responses, consider whether the longer wait is acceptable. For batch processing or offline analysis, latency is less of a concern. To mitigate delays, you can reduce the input context size by trimming unnecessary content or using smaller models for simpler parts of the workflow.
OrcaRouter bills GPT-5 Pro at the provider's rate with zero markup: $15.00 per 1 million input tokens and $120.00 per 1 million output tokens. Input tokens include all text, image tokens (calculated from resolution), and file content tokenized by the model. Output tokens are those generated by the model. The pricing is per-token, with no additional fees or surcharges. OrcaRouter does not add any hidden costs; the price you see is exactly what OpenAI charges. You can monitor token usage through OrcaRouter's dashboard or by checking the response headers. For cost-sensitive applications, consider token optimization strategies such as truncating inputs, using lower resolution images, or limiting output length via the max_tokens parameter.
The cost is justified when your task requires the full capacity of GPT-5 Pro—specifically, the 400K context window, 272K output limit, or multimodal input. Use cases like analyzing a complete legal framework in one go, generating a comprehensive report from many files, or building a conversational agent that remembers entire conversation histories are good candidates. If you are processing fewer than 100K tokens routinely, cheaper models likely suffice. Also consider that caching can reduce costs if you reuse the same input prefixes (though OrcaRouter's caching policy is standard). For high-volume, high-value tasks where accuracy and context are critical, GPT-5 Pro's cost may be acceptable.
OrcaRouter supports standard caching mechanisms for repeated prompts, similar to OpenAI's own system. When identical prompt prefixes are sent, the model may reuse cached intermediate states, reducing both latency and cost for subsequent calls. However, caching is not guaranteed and depends on the provider's implementation. Token optimization is the most effective cost-control tool: use the fewest tokens possible by trimming irrelevant context, lowering image resolution, and setting a moderate max_tokens. You can also split a large task into multiple smaller requests to cheaper models, reserving GPT-5 Pro only for the parts that need its full capabilities. OrcaRouter does not impose any additional caching fees; caching benefits are passed through.
GPT-5 Pro is the most expensive model available through OrcaRouter, with input cost 6 times higher than GPT-4o (approximately $2.50/1M input) and output cost roughly 5 times higher. Compared to GPT-3.5 Turbo, it is about 30 times more expensive for input and 40 times for output. This makes it a premium option for specialized use cases. Other providers' large models, such as Claude 3.5 Sonnet (around $3.00/1M input), are also significantly cheaper. The trade-off is that GPT-5 Pro offers the largest context and output limits among them. When choosing, consider not just the per-token price but the total cost of the task: a single GPT-5 Pro request might replace dozens of smaller model requests, potentially saving time and money if the smaller models would require retries or context splitting.
To use GPT-5 Pro, send a POST request to OrcaRouter's base URL https://api.orcarouter.ai/v1/chat/completions (or the completions endpoint for non-chat). Include your OrcaRouter API key in the Authorization header (Bearer YOUR_API_KEY). In the request body, set the model parameter to "openai/gpt-5-pro". For chat completions, provide messages in the standard OpenAI format. For image input, include a message with role "user" and content that contains both text and an image URL or base64 data. For file input, include the file content as part of the message. The API accepts the same parameters as OpenAI: temperature, top_p, max_tokens, stop, etc. Response format is also identical, including usage statistics.
GPT-5 Pro supports all standard parameters defined by OpenAI's chat completion API. These include temperature (0–2, typically 0–1), top_p, frequency_penalty, presence_penalty, max_tokens, stop sequences, and n (number of responses). Also supported are response_format (e.g., {"type": "json_object"}), seed for deterministic output, and tools/function calling. Note that the model's large context may affect response times when using long prompts. When using image or file inputs, ensure the message structure conforms to the multimodal format (e.g., content as an array of parts). OrcaRouter does not impose additional parameters beyond those passed through. For streaming, set stream: true in the request body.
Migration is straightforward because OrcaRouter's API is fully compatible with OpenAI's. If you already use an OpenAI SDK, simply change the base URL to https://api.orcarouter.ai/v1 and update the model ID to "openai/gpt-5-pro". Replace your API key with an OrcaRouter key. No other code changes are necessary for text-only requests. If you were using a different provider's large model, you may need to adjust prompts to match GPT-5 Pro's behavior. For multimodal inputs, follow OpenAI's format for images and files. You can test the migration by sending a small request with low max_tokens to verify connectivity and cost. OrcaRouter also supports fallback logic: you can set a default model and provider in the API call, allowing gradual rollout.
Authentication uses a Bearer token in the Authorization header. Obtain an API key from OrcaRouter's dashboard. Errors follow OpenAI's standard HTTP codes: 401 for invalid key, 429 for rate limits, 400 for bad request, 404 for invalid model, etc. The model ID "openai/gpt-5-pro" must be exact. If you receive a 404, ensure the model is enabled in your OrcaRouter account. Rate limits depend on your plan; check OrcaRouter's documentation for specifics. For large outputs, consider chunking or using streaming to handle partial responses. Also, the provider may reject requests that exceed the 400K context or 272K output limit—your request will be returned with an error. OrcaRouter will pass through the provider's error messages unchanged.
Compared to GPT-4o, GPT-5 Pro offers a substantially larger context window (400K vs typically 128K), longer max output (272K vs 16K), and support for image and file inputs (GPT-4o also supports images but not files in the same way). However, pricing is much higher: $15.00/1M input vs approximately $2.50/1M input for GPT-4o. GPT-5 Pro is not necessarily better for all tasks; for short queries, GPT-4o provides similar quality at a lower cost. The choice depends on whether your use case requires the extreme capacity. If your average context is under 100K tokens and you don't need output beyond 16K tokens, GPT-4o is more economical. OrcaRouter offers both, so you can switch per request.
Claude 3.5 Sonnet (from Anthropic) has a 200K token context window and supports images but not files. Its pricing is around $3.00/1M input and $15.00/1M output, significantly cheaper than GPT-5 Pro. GPT-5 Pro offers double the context length and a much larger output limit (272K vs typically 8K for Sonnet). However, Claude is known for strong reasoning on long documents and safety alignment. Neither model has public benchmarks side-by-side. Your choice may depend on output needs: if you need to generate very long responses, GPT-5 Pro is the only option. For analysis tasks where output is moderate but context is large, Claude 3.5 Sonnet could be more cost-effective. OrcaRouter supports both models for direct comparison.
Gemini 1.5 Pro offers a massive 1M token context window (with experimental up to 2M) and supports multimodal inputs including audio and video, which GPT-5 Pro does not. Pricing varies but is typically cheaper than GPT-5 Pro (around $3.50/1M input for text). However, Gemini's output limit is smaller (around 8K tokens). GPT-5 Pro's advantage is its superior output length (272K tokens) and its integration with OpenAI ecosystems. If your task requires generating extremely long text or code, GPT-5 Pro wins. If you need to process audio or very long documents with moderate output, Gemini 1.5 Pro is a strong contender. Both are available on OrcaRouter, allowing direct comparison.
Strengths: Largest context window among OpenAI models (400K), highest output limit (272K), multimodal (image, text, file), and seamless compatibility with OpenAI tools. It is the only model on OrcaRouter that combines all these features. Weaknesses: High cost ($15.00/1M input, $120.00/1M output), lack of public benchmarks, no audio or video input (unlike Gemini), and potentially higher latency due to large context processing. Compared to cheaper alternatives, it is overkill for short or simple tasks. The model's performance on nuanced reasoning has not been independently verified. For enterprise use, consider whether the additional capacity justifies the expense over a well-optimized smaller model. OrcaRouter's flexible routing lets you balance cost and capability.
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",
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-proOpen @misc{orcarouter_gpt_5_pro,
title = {GPT-5 Pro API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5-pro}
}OpenAI. (2025). GPT-5 Pro API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5-pro