OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning...
OpenAI GPT-4 is a large language model engineered for text-based tasks. It accepts only text inputs and generates text outputs. The model has a context window of 8,191 tokens and a maximum output…
GPT-4 demonstrates strong performance in reasoning, creative writing, summarization, translation, and code generation. It can handle complex instructions and produce coherent, detailed responses across many domains. On the AA Coding benchmark, GPT-4 scores 13.1, indicating effective problem-solving in programming tasks. The model is also known for its ability to follow nuanced prompts and maintain consistency over long conversations. This makes it suitable for applications like chatbots, content creation, and assisted software development. However, it is not optimized for real-time streaming or extremely low-latency requirements.
GPT-4 excels at tasks that require deep understanding and generation of natural language. Common use cases include writing essays or reports, drafting emails, generating and debugging code in multiple languages, creating summaries of long documents, answering questions based on provided context, and simulating conversational agents. It can also handle classification, extraction, and reasoning tasks. For tasks that involve only text and fit within the 8,191-token context window, GPT-4 provides high-quality output. It is less suited for real-time applications with strict latency constraints or for handling very large contexts beyond its token limit.
If your application requires only simple, short outputs, or if you are performing bulk processing where cost is a primary concern, you might opt for a more economical model available on OrcaRouter, such as GPT-3.5-Turbo. GPT-4's pricing of $30 per 1M input tokens is significantly higher than many alternatives. For tasks like basic classification, keyword extraction, or simple Q&A, a smaller model can deliver comparable results at lower cost. Also, if your use case does not demand the reasoning depth of GPT-4, a lighter model may be sufficient. OrcaRouter lets you test multiple models and compare cost/quality trade-offs.
Yes. GPT-4 is capable of generating code in various programming languages, including Python, JavaScript, C++, and more. It can also assist with debugging by analyzing error messages or code snippets. The model's AA Coding score of 13.1 reflects its ability to solve coding challenges. For example, you can provide a problem description and receive a working solution, or ask GPT-4 to review existing code for bugs or inefficiencies. However, always verify generated code for correctness and security, as models can produce plausible-looking but flawed code.
The AA Coding benchmark evaluates a model's performance on programming tasks, typically involving writing code to solve algorithmic problems. GPT-4 achieves a score of 13.1 on this benchmark. This number indicates the average performance across a set of coding challenges, with higher scores representing better accuracy and completeness. For context, human expert performance on such benchmarks is often higher, but GPT-4's score places it among top-tier models for code generation. This benchmark is one indicator of the model's reasoning and problem-solving ability in a technical domain.
While specific numbers beyond AA Coding are not provided here, GPT-4 is known to achieve strong results across a variety of NLP benchmarks, including language understanding, translation, and summarization tasks. It generally outperforms earlier models like GPT-3.5 on most evaluations. However, performance can vary depending on the exact task and dataset. The model's 8,191-token context window allows it to process relatively long inputs, which can be beneficial for tasks requiring document-level reasoning. On OrcaRouter, you can test GPT-4 on your own data to assess its suitability.
Latency for GPT-4 depends on the length of input and output tokens, as well as overall API load. Typically, GPT-4 is slower than smaller models like GPT-3.5 because of its larger size and more complex computations. For short prompts, response times may be a few seconds; for longer outputs, it can take tens of seconds. OrcaRouter provides a standard API endpoint that streams responses if you set stream=True. For real-time applications where low latency is critical, consider using a faster model. Exact latency figures are not published but can be estimated through testing.
GPT-4 has several limitations. It only processes text, so it cannot handle images or audio directly. Its context window of 8,191 tokens means it cannot process very long documents in one go. The model may produce plausible but incorrect information (hallucination) and can be sensitive to prompt phrasing. It also has no inherent memory beyond the current conversation context. Pricing is relatively high compared to smaller models. Finally, like all LLMs, it can generate biased or unsafe content if not properly guided. Use appropriate moderation and validation in production systems.
OrcaRouter charges the exact provider rate for GPT-4 with zero markup. Input tokens cost $30.00 per 1 million tokens, and output tokens cost $60.00 per 1 million tokens. Both input and output tokens are billed separately. This means the total cost for a request is (input_tokens * $30/1M) + (output_tokens * $60/1M). For example, a request with 1000 input tokens and 500 output tokens would cost 0.001 * $30 + 0.0005 * $60 = $0.03 + $0.03 = $0.06. There are no additional fees from OrcaRouter beyond the usage-based billing.
OrcaRouter may offer caching options for repeated prompts or completions, which can reduce costs if you send identical requests multiple times. Check OrcaRouter's documentation for details on caching and how to enable it. For GPT-4, because pricing is per token and relatively high, caching can be beneficial for static queries. Additionally, you can control costs by managing token usage: keep prompts concise, use shorter outputs, and set max_tokens appropriately. There are no volume discounts on OrcaRouter for GPT-4; pricing is uniform per million tokens.
GPT-4 is among the more expensive models available on OrcaRouter. For comparison, GPT-3.5-Turbo is significantly cheaper (often less than $1 per 1M input tokens). Smaller models from other providers are also more economical. If your task does not require GPT-4's level of reasoning, switching to a cheaper model can result in substantial savings. OrcaRouter allows you to test multiple models with the same API interface, so you can evaluate quality versus cost. The zero-markup policy means you only pay the provider rate; there's no premium for using OrcaRouter.
Yes. OrcaRouter operates on a pay-as-you-go basis. You are billed only for the tokens you use. There are no upfront commitments or minimum usage requirements. You need to have sufficient credits or a funded account to make API calls. Pricing is per 1M tokens as described. For budget planning, you can estimate costs based on your expected token volume. OrcaRouter provides usage analytics in the dashboard. Note that requests may fail if your account balance is insufficient.
To call GPT-4, use the OpenAI-compatible API endpoint provided by OrcaRouter: base URL https://api.orcarouter.ai/v1. Set the model parameter to "openai/gpt-4". You can use any OpenAI client library by changing the base_url. Example using Python openai package: ```python import openai client = openai.OpenAI(api_key="YOUR_ORCAROUTER_KEY", base_url="https://api.orcarouter.ai/v1") response = client.chat.completions.create( model="openai/gpt-4", messages=[{"role": "user", "content": "Hello"}] ) print(response.choices[0].message.content) ``` Remember to replace YOUR_ORCAROUTER_KEY with your actual API key.
You can use the standard OpenAI chat completion parameters: `messages`, `max_tokens`, `temperature`, `top_p`, `frequency_penalty`, `presence_penalty`, `stop`, `stream`, etc. The `model` must be `"openai/gpt-4"`. The `max_tokens` parameter should not exceed 8192 minus the input tokens. `temperature` controls randomness (0 to 2). `stream` set to `true` enables token-by-token streaming. All parameters are passed in the JSON body. OrcaRouter supports the same schema as OpenAI. Refer to OpenAI's API documentation for detailed parameter descriptions.
Yes. If you already use the OpenAI Python library or any SDK compatible with OpenAI's API, you only need to change the `base_url` to `https://api.orcarouter.ai/v1` and update your API key. The model ID changes from "gpt-4" to "openai/gpt-4". No other code changes are required. OrcaRouter's API is designed to be a drop-in replacement for the OpenAI API. This makes migration straightforward. You can test with a small number of requests to verify behavior before switching production traffic.
Yes. Set the `stream` parameter to `true` in your API request. The response will be an iterator of chat completion chunks, following the OpenAI streaming format. This allows you to display tokens as they are generated. Streaming can improve perceived latency for end users. OrcaRouter handles the streaming natively. Just ensure your client code handles the stream correctly. Example using OpenAI Python library with `stream=True` yields a `Stream` object.
GPT-4 is generally more capable in reasoning, creativity, and following complex instructions compared to GPT-3.5-Turbo. On benchmarks like AA Coding, GPT-4 outperforms GPT-3.5 (which has lower scores). However, GPT-4 is also more expensive ($30/$60 per 1M tokens vs. much lower for GPT-3.5). GPT-3.5 has a larger context window (16,384 tokens) compared to GPT-4's 8,191. If your task is simple or budget-sensitive, GPT-3.5 may be sufficient. For complex coding, long-form writing, or nuanced analysis, GPT-4 gives better results. Both are text-only models available on OrcaRouter.
Claude models (e.g., Claude 3 Sonnet or Opus) offer larger context windows (up to 200K tokens) and multimodal input in some versions. GPT-4 is text-only and limited to 8,191 tokens. Claude models are also priced differently. On reasoning benchmarks, both families are competitive, but GPT-4's AA Coding score of 13.1 may be higher or lower than specific Claude variants. Choosing between them depends on your specific needs: Claude for long documents or multimodal, GPT-4 for high-performance text generation within its context limit. OrcaRouter supports both, so you can compare directly.
Open-source models like Llama 3 (70B) can sometimes match GPT-4 on certain tasks but often require more complex hosting and have different trade-offs. GPT-4 is proprietary but available via simple API with predictable pricing. Llama 3 may have larger context windows or lower per-token cost if self-hosted, but infrastructure costs apply. GPT-4's consistent quality and zero-markup pricing via OrcaRouter make it easy to use. For custom fine-tuning, open-source models offer more flexibility. OrcaRouter also provides access to some open-source models for comparison.
The value of GPT-4 depends on your use case. If you need top-tier reasoning, code generation, or creative output, the higher cost may be justified. In scenarios where mistakes are costly (e.g., legal drafting, complex debugging), GPT-4's accuracy can save time and money. For simpler tasks, cheaper models like GPT-3.5 provide adequate performance at a fraction of the price. OrcaRouter allows you to A/B test models on your data to quantify the quality difference. Ultimately, the decision is a cost-quality trade-off: use GPT-4 when its output quality translates to tangible business value.
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-4",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_tokenspresence_penaltyresponse_formatseedstopstreamtemperaturetool_choicetoolstop_logprobstop_p| Input / 1M tokens | $30.00 |
| Output / 1M tokens | $60.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_gpt_4,
title = {GPT-4 API},
author = {OpenAI},
year = {2023},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4}
}OpenAI. (2023). GPT-4 API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4