GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...
GPT-4o is a multimodal large language model by OpenAI. It accepts text, image, and file inputs, and outputs text. The model is designed for users who need to process lengthy content (up to 128,000…
GPT-4o excels in multimodal reasoning, long-context processing, and mathematical problem solving. Its 128k-token context allows it to retain and reference large amounts of information, making it useful for tasks like document analysis, code generation across entire projects, and complex multi-turn conversations. The model scored 75.9 on the MATH-500 benchmark, indicating strong capability in solving mathematical problems. Additionally, its ability to accept image and file inputs means it can reason about visual content—such as diagrams, charts, or photographs—in combination with text. On OrcaRouter, you can leverage these strengths without paying any markup over OpenAI’s listed prices.
GPT-4o is highly effective for tasks that require both textual and visual understanding. Examples include: answering questions about a diagram in a textbook, extracting data from a scanned invoice, generating code based on a wireframe image, or summarizing a long research paper with figures. Its large context also makes it suitable for analyzing entire codebases or lengthy conversation logs. Mathematical reasoning is another strong suit, as shown by its 75.9 on MATH-500. On OrcaRouter, these use cases are straightforward to implement—you send a standard OpenAI-compatible request with the appropriate content types, and the model returns a text response.
If your task does not require multimodal input or a large context, a cheaper model could be more cost-effective. For example, for simple text classification, summarization, or translation tasks that fit within a smaller context, models like GPT-3.5 Turbo (priced at $0.50 per 1M input and $1.50 per 1M output) are significantly cheaper. GPT-4o’s input cost is $2.50 per 1M tokens, which is five times higher than GPT-3.5 Turbo. If you are processing large volumes of text-only data without needing multimodal reasoning, a smaller model can reduce costs. OrcaRouter offers a variety of models so you can choose the best price-performance balance.
GPT-4o is designed to accept images and files as inputs, enabling it to perform tasks like visual question answering, image captioning, and document understanding. While specific multimodal benchmark scores are not provided in the available facts, the model’s architecture supports joint reasoning over text and images. For example, it can count objects in an image or interpret a chart. The model outputs only text, not images. On OrcaRouter, multimodal requests are sent using the same parameters as OpenAI’s API: include an image_url or file content part in the messages array. The model processes the visual data and returns a text response based on the combined input.
GPT-4o achieves a score of 75.9 on the MATH-500 benchmark. MATH-500 is a subset of the MATH dataset, which consists of 500 challenging math problems across various topics such as algebra, geometry, and calculus. A score of 75.9 means the model correctly solves approximately 76% of these problems. This indicates strong mathematical reasoning ability, though not perfect. For comparison, many smaller models score below 50 on similar benchmarks. This score is a reflection of the model’s ability to perform multi-step reasoning and handle mathematical notation. On OrcaRouter, you can use GPT-4o for math-related tasks like tutoring, test preparation, or verifying calculations.
Like all large language models, GPT-4o has limitations. It may produce incorrect or hallucinated answers, especially on topics with limited training data or requiring very recent knowledge (its training cutoff date is not provided in the facts, but as an OpenAI model it has a knowledge cutoff). It can be sensitive to prompt phrasing and may struggle with ambiguous instructions. Despite its strong MATH-500 score, it is not infallible on complex mathematical proofs. The model processes images but does not generate images. Additionally, the large context window (128k tokens) can lead to increased token usage, raising costs if not managed carefully. On OrcaRouter, you can mitigate some issues by adjusting parameters like temperature and top_p.
Exact latency figures for GPT-4o are not provided in the facts. In general, GPT-4o is a large model and will have higher latency compared to smaller models like GPT-3.5 Turbo or Llama 3.1 8B. However, it is likely optimized for faster inference than the original GPT-4. Through OrcaRouter, you can expect response times similar to what you would get from OpenAI directly, as the platform routes requests without additional processing. For real-time applications where speed is critical, consider using a faster, cheaper model for simpler tasks and reserving GPT-4o for complex queries that benefit from its capabilities.
On OrcaRouter, GPT-4o is priced at exactly OpenAI’s listed rates: $2.50 per 1 million input tokens and $10.00 per 1 million output tokens. There is zero markup added by OrcaRouter. Billing is based on token counts rounded to the nearest whole token. Input tokens include the text, images, and files you send, though images are typically tokenized at a fixed rate by the model (e.g., each image consumes a certain number of tokens). Output tokens are the text generated by the model. You are charged only for what you use; there are no monthly fees or minimum commitments. This transparent pricing allows you to budget accurately for your AI workloads.
The primary trade-off is cost versus capability. GPT-4o costs $2.50 input and $10 output per 1M tokens. Cheaper models like GPT-3.5 Turbo ($0.50 input, $1.50 output) or Llama 3.1 8B are more economical for simple or high-volume tasks. However, GPT-4o offers multimodal input and a larger context window. If your application requires understanding images or processing very long documents, GPT-4o may be necessary despite the higher cost. For text-only tasks with short inputs, a cheaper model can save money. On OrcaRouter, you can switch between models with a simple change of the model ID, so you can optimize cost for each request.
No. OrcaRouter bills GPT-4o at the exact provider rate with zero markup. The prices are $2.50 per 1M input tokens and $10.00 per 1M output tokens. There are no hidden fees, upsells, or additional charges. OrcaRouter passes through the cost directly from OpenAI. This policy applies to all models listed on the platform. The base URL is https://api.orcarouter.ai/v1 and the model ID is "openai/gpt-4o". You can verify the pricing at any time by checking your usage dashboard. There is no tiered pricing or commitment required—pay only for tokens consumed.
To use GPT-4o through OrcaRouter, send HTTP POST requests to the base URL https://api.orcarouter.ai/v1/chat/completions. Set the model parameter to "openai/gpt-4o". Your request must include an API key (provide your OrcaRouter API key in the Authorization header). The request body follows the standard OpenAI chat completions format: a messages array with role/content pairs, optional parameters like temperature, max_tokens, top_p, etc. For multimodal inputs, include content parts that have a type of "text" or "image_url" (or file). The response is a JSON object containing the model's completion. Example curl command: curl https://api.orcarouter.ai/v1/chat/completions -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"model":"openai/gpt-4o","messages":[{"role":"user","content":[{"type":"text","text":"What is in this image?"},{"type":"image_url","image_url":{"url":"https://example.com/image.jpg"}}]}]}'
When calling GPT-4o via OrcaRouter, you can use the same parameters as OpenAI's API. Commonly used parameters include: - temperature (float, 0-2): Controls randomness. Lower values like 0.2 make output more deterministic. - max_tokens (integer, default 16384): Maximum number of tokens in the response. - top_p (float, 0-1): Nucleus sampling parameter. - frequency_penalty and presence_penalty: Reduce repetition. - stop (string or array): Stop sequences. - stream (boolean): Enable streaming responses (true/false). - seed (integer): For deterministic sampling. You can also use the optional "user" field for tracking individual users. The model supports function calling and tool use, though not explicitly listed in the facts. On OrcaRouter, all standard parameters are accepted.
Migrating is straightforward because OrcaRouter offers an OpenAI-compatible endpoint. Steps: 1. Obtain an API key from OrcaRouter (sign up at their website). 2. Change the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1. 3. Change the model ID from "gpt-4o" to "openai/gpt-4o". 4. Use the same request body format. No code changes other than the endpoint and model name are needed. 5. Update the Authorization header to use your OrcaRouter API key. Testing with a single request is recommended. OrcaRouter uses the same streaming and non-streaming modes. There is no difference in how the model behaves, as it is the same underlying OpenAI model. Your existing prompts and parameters will work identically.
The base URL for the OrcaRouter API is https://api.orcarouter.ai/v1. The specific model ID to use for GPT-4o is "openai/gpt-4o". This exact string must be passed in the "model" parameter of the request. For example, in a chat completions endpoint call, you set "model": "openai/gpt-4o". OrcaRouter supports this model for both streaming and non-streaming completions. If you are using an OpenAI SDK or library configured to point to a custom base URL, simply set the base URL to https://api.orcarouter.ai/v1 and provide your OrcaRouter API key. The model ID prefix "openai/" distinguishes it from other providers' models on OrcaRouter.
GPT-4o is the multimodal successor to GPT-4 Turbo. Both models have a 128k context window. However, GPT-4o adds native image and file input support, whereas GPT-4 Turbo was primarily text-only (with vision available only in separate versions). GPT-4o also has a higher max output of 16,384 tokens compared to GPT-4 Turbo's typical 4,096 or 8,192. Pricing differs: GPT-4 Turbo costs $10.00 per 1M input and $30.00 per 1M output, making GPT-4o significantly cheaper ($2.50/$10.00). The MATH-500 score of 75.9 for GPT-4o is generally higher than GPT-4 Turbo's reported benchmarks, though exact Turbo scores are not in the facts. On OrcaRouter, both models are available with zero markup.
GPT-3.5 Turbo is a cheaper, faster model optimized for simple tasks. Its context window is 16,384 tokens, much smaller than GPT-4o's 128k. GPT-3.5 does not support image inputs—it is text-only. Its pricing is $0.50 per 1M input and $1.50 per 1M output tokens, making it roughly 5x cheaper for inputs and 6.6x cheaper for outputs than GPT-4o. On the MATH-500 benchmark, GPT-3.5 Turbo typically scores much lower (exact numbers not provided). GPT-4o is better suited for complex reasoning, long documents, and multimodal tasks. Use GPT-3.5 Turbo for high-volume, simple interactions. On OrcaRouter, you can easily switch between them by changing the model ID.
Claude 3.5 Sonnet is a competing model from Anthropic. It also has a 200k context window (larger than GPT-4o's 128k) and supports image inputs. Pricing for Claude 3.5 Sonnet is typically $3.00 per 1M input and $15.00 per 1M output, making GPT-4o cheaper on input but more expensive on output? Actually GPT-4o is $2.50 input vs $3.00, so cheaper; output $10 vs $15, so cheaper. On benchmarks, Claude 3.5 Sonnet achieves similar performance on math and reasoning but exact scores vary. Both models are strong in multimodal tasks. The choice between them may depend on specific performance characteristics or ecosystem preferences. On OrcaRouter, both are accessible via the same API format with different model IDs (e.g., "anthropic/claude-3.5-sonnet").
Llama 3.1 405B is an open-weight model by Meta with 405 billion parameters. It also supports a 128k context window and text input, but its multimodal capabilities are less clear (it may not natively support images). Llama 3.1 405B is available through OrcaRouter at a lower price point, typically around $1.00 per 1M input and $1.00 per 1M output tokens (approximate, not provided in facts). Its benchmark scores on MATH-500 are not given but are likely lower than GPT-4o's 75.9. GPT-4o is proprietary and offers better out-of-the-box multimodal reasoning and potentially higher accuracy on complex tasks. Llama 3.1 405B is a good choice for users who want an open model or need to self-host. On OrcaRouter, you can compare both models side-by-side.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokensnparallel_tool_callspredictionpresence_penaltyresponse_formatseedservice_tierstopstreamstructured_outputstemperaturetool_choicetoolstop_logprobstop_pweb_search_options| Input / 1M tokens | $2.50 |
| Output / 1M tokens | $10.00 |
| Cache read / 1M | $1.25 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
@misc{orcarouter_gpt_4o,
title = {GPT-4o API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o}
}OpenAI. (2024). GPT-4o API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o