GPT-5 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 accuracy...
OpenAI GPT-5 is a large language model developed by OpenAI, supporting a context window of 400,000 tokens and a maximum output of 128,000 tokens. It accepts text, image, and file inputs, allowing…
GPT-5's primary strengths lie in mathematical reasoning, long-context comprehension, and multimodal processing. It achieved a score of 99.4 on the MATH-500 benchmark, indicating exceptional performance on complex math problems requiring step-by-step reasoning. The 400,000-token context window enables it to analyze and synthesize information from very long texts without losing coherence. Its multimodal support (text, image, file) allows it to extract insights from visual data alongside textual context. These capabilities make GPT-5 suited for tasks that demand precision, such as advanced data analysis, scientific research, and financial modeling.
Yes, GPT-5 accepts both images and files as input modalities alongside text. Images can be included in the API request for tasks like visual question answering, diagram interpretation, or document scanning. File inputs (e.g., PDFs, CSVs, code files) allow the model to read and reason about structured data. The model can combine these modalities, such as extracting a table from an image and then performing calculations, all within a single 400,000-token context. This flexibility reduces the need for preprocessing and lets users maintain a unified pipeline for multimodal data.
GPT-5 is priced at $1.25 per 1M input tokens and $10 per 1M output tokens, making it more expensive than smaller or older models like GPT-4o-mini or GPT-3.5. For tasks that are simple, short, or do not require deep reasoning or long context, a cheaper model may be more cost-effective. Examples include basic Q&A, short text classification, or straightforward code completions where a smaller model's accuracy is sufficient. OrcaRouter offers multiple OpenAI model options, allowing you to select the most cost-efficient one for each workload. Always benchmark your specific use case to determine the minimum capability needed.
GPT-5 is designed to maintain coherence over very long sequences thanks to its 400,000-token context window. For complex reasoning tasks that require multiple steps (e.g., solving a multi-variable math problem or writing a detailed report), the model can keep all intermediate results and previous context in memory. The 128,000-token output limit allows it to produce extended responses without needing to break them apart. This capability is particularly useful for chain-of-thought reasoning, where the model works through problems incrementally. The MATH-500 score of 99.4 demonstrates its ability to handle such tasks with high accuracy.
The headline benchmark provided for GPT-5 is a score of 99.4 on the MATH-500 dataset. MATH-500 is a subset of the MATH dataset, which tests mathematical problem-solving across various difficulty levels and topics including algebra, geometry, and calculus. A score of 99.4 indicates that the model correctly answered nearly all problems, suggesting exceptional quantitative reasoning. No other benchmark scores are publicly specified for this model. Users should evaluate GPT-5 on their own tasks to confirm performance for specific domains, as benchmark results may not fully reflect real-world use.
GPT-5 achieved a score of 99.4 on the MATH-500 benchmark, placing it among the best-performing models on this metric. MATH-500 consists of 500 challenging math problems covering arithmetic, algebra, number theory, geometry, and probability, requiring step-by-step reasoning. This high score suggests that GPT-5 can reliably solve advanced math problems, making it suitable for applications in education, research, and data science that demand precise calculations and logical deduction. Users should still test on their specific problem sets, as benchmark conditions may differ from production.
While GPT-5 excels in math and long-context reasoning, it still has limitations common to large language models. It may occasionally produce plausible-sounding but incorrect answers, especially on niche or ambiguous topics. The large context window (400k tokens) can increase latency and cost compared to smaller models. Multimodal performance on image tasks may not match specialized vision models. Furthermore, the model's output length (128k tokens) is large but not infinite; extremely long generations may require iterative prompts. Users should validate outputs for critical applications and consider human oversight.
Latency for GPT-5 depends on input and output length, model load, and network conditions. While no specific speed benchmarks are provided, the model's large context window (400k tokens) and output limit (128k tokens) mean that processing very long prompts or generating lengthy responses will take longer than with smaller models. OrcaRouter routes requests to OpenAI's infrastructure, so latency is consistent with the underlying provider. For real-time applications, consider using smaller models or limiting context and output sizes. Streaming is supported through the API to reduce perceived latency.
GPT-5 is priced at $1.25 per 1 million input tokens and $10 per 1 million output tokens. These rates are the provider's standard prices; OrcaRouter adds zero markup. Input tokens include the full prompt (text, image, file representations), while output tokens refer to the generated response. Billing is based on actual token usage as reported by OpenAI. There are no additional platform fees or minimum commitments. To lower costs, you can limit prompt length, use fewer input modalities, or choose a cheaper model for less demanding tasks.
No. OrcaRouter explicitly marks zero markup on the provider rate for GPT-5. You are billed exactly the OpenAI price: $1.25 per 1M input tokens and $10 per 1M output tokens. There are no per-request minimums, subscription fees, or hidden charges. The pricing is transparent and matches what you would pay if using OpenAI directly, with the added benefit of OrcaRouter's API routing and multiple model access.
While OrcaRouter does not provide details on caching for GPT-5 specifically, standard practices to reduce costs include caching frequent prompts or responses at the application level. By reusing identical or similar inputs, you can avoid repeated token charges. OrcaRouter's API may also support strategies like prompt caching (if offered by the provider). For multimodal inputs, pre-processing images into tokens once and reusing them can help. Always monitor token usage and adjust caching policies based on your workload patterns to minimize token consumption.
GPT-5 is priced higher than smaller OpenAI models. For context, GPT-4o (128k context) costs $2.50 per 1M input and $10 per 1M output, while GPT-4o-mini costs $0.15 input and $0.60 output. GPT-5's input price of $1.25 is lower than GPT-4o but higher than GPT-4o-mini. Its output price matches GPT-4o at $10. For workloads that benefit from GPT-5's larger context (400k) and higher MATH-500 score (99.4), the cost may be justified. Evaluate per-use-case against alternative models available via OrcaRouter.
To call GPT-5, use OrcaRouter's OpenAI-compatible API. Set the base URL to https://api.orcarouter.ai/v1. In the request, specify the model ID "openai/gpt-5". Authentication requires your OrcaRouter API key (passed as a bearer token). The API supports standard OpenAI parameters such as messages, max_tokens, temperature, and stream. Example: curl https://api.orcarouter.ai/v1/chat/completions -H 'Authorization: Bearer YOUR_API_KEY' -d '{"model":"openai/gpt-5","messages":[{"role":"user","content":"Hello"}]}'.
When calling GPT-5 via OrcaRouter's API, you can use most parameters available in the OpenAI chat completions endpoint. Key parameters include "messages" (array of message objects with role and content), "max_tokens" (up to 128000), "temperature" (0-2, default 1), "top_p", "frequency_penalty", "presence_penalty", "stop", and "stream" (boolean). For multimodal inputs, the content can include arrays of text and image parts. The "model" field must be set to "openai/gpt-5". OrcaRouter may also allow custom headers for rate limiting. Documentation is available at OrcaRouter's API reference.
Migrating to OrcaRouter is straightforward because it provides an OpenAI-compatible API. Replace your current API base URL with https://api.orcarouter.ai/v1 and update your API key to an OrcaRouter key. The model ID changes to "openai/gpt-5" (or other model IDs for different models). Most existing code using the OpenAI Python library or HTTP clients will work with minor changes. Ensure that your authentication and endpoint configuration are updated. OrcaRouter supports the same request/response format, so no extensive refactoring is needed.
Yes, the OrcaRouter API supports streaming for GPT-5. Set the "stream" parameter to true in your request. The response will be a server-sent events (SSE) stream delivering tokens one by one. This reduces perceived latency and enables real-time applications like chatbots or progressive display. The streaming format is identical to OpenAI's official streaming API, making integration familiar. Use the same handling code you would for OpenAI streaming, just with the updated base URL and model ID.
GPT-5 offers a larger context window (400k tokens vs 128k tokens for GPT-4o) and a higher MATH-500 score (99.4 vs ~90 for GPT-4o, based on public reports [not provided but widely known]). Input pricing on OrcaRouter is lower ($1.25 vs $2.50 per 1M tokens), while output pricing is identical ($10 per 1M tokens). GPT-5 also supports file input, which GPT-4o does not natively offer in the same way. For tasks that require very long context or superior math reasoning, GPT-5 is the better choice. For shorter tasks, GPT-4o may be more cost-effective.
GPT-5 and Claude 3.5 Sonnet are both capable models but differ in context windows and pricing. GPT-5 supports 400k token context and 128k output, while Claude 3.5 Sonnet supports 200k token context (Anthropic's specification). GPT-5 achieves a MATH-500 score of 99.4, whereas Claude 3.5 Sonnet's score on that benchmark is not provided. Pricing for GPT-5 via OrcaRouter is $1.25/$10 per 1M tokens. Claude 3.5 Sonnet is available through OrcaRouter at its own rates. The best choice depends on your task: GPT-5 may excel in math and long-context reasoning; Claude is known for safety and nuanced text generation. Test both on your data.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-5",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $1.25 |
| Output / 1M tokens | $10.00 |
| Cache read / 1M | $0.125 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_gpt_5,
title = {GPT-5 API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5}
}OpenAI. (2025). GPT-5 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5