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 (2024-05-13) is a multimodal language model from OpenAI, accessible through OrcaRouter’s API. It processes text, images, and files, making it suited for tasks that mix visual and textual data.…
GPT-4o can analyze images provided as part of the input. It can describe visual content, answer questions about objects, text within images, and spatial relationships. It can also extract and transcribe text from scanned documents or photos. Because it processes images directly, you do not need a separate OCR or vision model. The image is tokenized to fit within the context window. For instance, a user could upload a screenshot of a graph and ask the model to interpret the trends. The model’s understanding is not limited to simple captions; it can reason about the content, compare multiple images, and use visual cues alongside textual instructions. This capability is integrated into the same API call, using the same message format as text-only requests. On OrcaRouter, you send images as base64-encoded data or URLs in the content array. The model ID remains "openai/gpt-4o-2024-05-13".
GPT-4o accepts files as input. Supported formats include PDF, Microsoft Office documents (Word, Excel, PowerPoint), text files, and image formats. For PDFs and Office files, the model extracts textual content and analyzes the layout. It does not directly render complex formatting but reads the text content. This is useful for processing reports, contracts, spreadsheets, or presentations without manual extraction. The file content is tokenized and counts toward the input token total. The model can answer questions about the file contents, summarize them, or perform calculations on tabular data. When using files, you include them as part of the message content using the appropriate OpenAI API fields. OrcaRouter handles the transmission without modification. Note that the model’s capabilities depend on the quality of the extracted text; heavily scanned images in PDFs may not be fully readable unless they contain embedded text layers.
GPT-4o is a premium model with higher cost than alternatives like GPT-4o-mini or earlier GPT-3.5 variants. If your task does not require the multimodal capabilities (e.g., text-only tasks), large context (up to 128K), or the mathematical reasoning level measured by MATH-500 (79.1), a cheaper model may be more economical. For instance, simple classification, short-form content generation, or basic chat can be handled by lower-cost models that still produce acceptable quality. Also, if your application is latency-sensitive and the smaller model is faster, the trade-off may favor speed over absolute accuracy. Finally, if you rarely need to process images or files, the added multimodal capability is unnecessary. OrcaRouter offers a range of models so you can select the best price-performance ratio. Evaluate your use case’s requirements against the benchmark scores and pricing to determine whether GPT-4o’s advantages justify the additional cost.
GPT-4o can generate up to 16,384 tokens per request. This is a generous limit that allows for long-form answers, detailed code, or multi-paragraph analyses. However, output tokens are billed at $15.00 per million, making longer outputs more expensive. You can control the output length using the max_tokens parameter in the API call. If you anticipate needing very long generations, consider batching or splitting the request. The 16,384 limit applies to the entire completion, including reasoning steps or chain-of-thought if requested. For very complex tasks that require extended thinking, you may need to use multiple calls. On OrcaRouter, the output token count is reported in the API response’s usage field, so you can track costs accurately. There is no additional limit imposed by OrcaRouter; the model’s native cap applies.
GPT-4o scored 79.1 on the MATH-500 benchmark. MATH-500 consists of 500 challenging mathematics problems across various topics such as algebra, geometry, number theory, and probability. A score of 79.1 means the model answered 79.1% of the problems correctly. This is a strong result that indicates robust mathematical reasoning ability, especially for a multimodal model. The benchmark tests both problem-solving and step-by-step reasoning. This score can guide expectations for applications involving math tutoring, scientific computation, or logic puzzles. Note that benchmark performance does not guarantee identical results in real-world tasks; domain-specific tuning or prompt engineering may be required. When considering this model, compare its MATH-500 score against other models you evaluate. OrcaRouter does not provide additional benchmark data, so this is the sole reference point provided for this model.
Latency depends on several factors: the length of the input, the length of the expected output, current load on OpenAI’s servers, and the network path between your application and OrcaRouter. OrcaRouter does not add significant overhead beyond the underlying provider response time. For typical requests with moderate input (a few thousand tokens) and short outputs (under 1,000 tokens), responses often arrive within a few seconds. Longer outputs (close to 16,384 tokens) will naturally take more time because the model generates tokens sequentially. Image inputs also increase latency because of tokenization and processing. You can optimize latency by reducing output length, using shorter prompts, or batching requests. OrcaRouter provides standard API endpoints that return results asynchronously via streaming if desired. For real-time interactive applications, consider using the streaming mode (stream: true) to begin processing as soon as tokens arrive. No specific latency numbers are provided in the model facts, so these are qualitative estimates.
Strengths: Multimodal input (text, image, file), large 128K context window, strong mathematical reasoning (MATH-500: 79.1), and high output token limit (16,384). It handles long documents and multi-turn conversations effectively. The API is standard OpenAI-compatible, easing integration. Limitations: Higher cost compared to smaller models. Not optimized for tasks that do not benefit from multimodality or large context. No information is provided about non-English language performance or specific safety benchmarks. Image understanding may be limited for very complex scenes or low-resolution images. Also, the model may produce incorrect answers on problems outside its training distribution. There is no disclosed latency guarantee. For users requiring extremely low latency or extremely low cost, a different model may be better. The benchmark score of 79.1 on MATH-500 indicates room for improvement on the hardest math problems. Evaluate whether these trade-offs align with your application’s requirements.
GPT-4o is billed per token, with separate rates for input and output tokens. Input tokens are $5.00 per 1 million tokens. Output tokens are $15.00 per 1 million tokens. These are the provider rates, passed through by OrcaRouter with zero markup. The cost per request = (input tokens/1e6 * 5) + (output tokens/1e6 * 15). For example, a conversation with 4,000 input tokens and 200 output tokens would cost (0.004*$5) + (0.0002*$15) = $0.02 + $0.003 = $0.023. There are no additional fees for API calls themselves; you only pay for tokens consumed. Pricing is updated dynamically based on provider changes; OrcaRouter passes along any future adjustments. Since there is no markup, users benefit from the same rates as direct OpenAI customers, but with the convenience of OrcaRouter’s unified management and billing. Be mindful that images and files are tokenized and contribute to the input token count, often increasing cost compared to text-only inputs.
No. OrcaRouter does not offer discounts, cached tokens, or reduced pricing for repeated inputs. Every token is billed at the standard provider rate. There is no separate caching cost or any surcharge for high volume. The $5/$15 per million tokens are flat rates. Users who require very high throughput may inquire about custom arrangements, but the standard service does not include volume discounts. Additionally, there are no hidden fees such as connection charges or monthly minimums. The pricing is transparent and directly tied to token usage. For cost optimization, consider reducing input size (e.g., trimming history, using shorter prompts) and output length (e.g., setting a lower max_tokens). Also, using the model only when its capabilities are necessary (multimodal, large context) helps control costs. If your application has repetitive prompts that are identical each time, caching at the application layer may reduce token usage, but OrcaRouter itself does not provide such a feature.
GPT-4o is premium-priced. For many tasks, a cheaper model (e.g., GPT-4o-mini or GPT-3.5-turbo) might be sufficient. Trade off by evaluating the complexity and required accuracy. If your task involves simple extraction or classification with limited context, a cheaper model may perform similarly at a fraction of the cost. Conversely, if the task requires nuanced understanding of images, long documents, or high mathematical precision (MATH-500 score of 79.1 versus lower scores on cheaper models), GPT-4o may be worth the premium. Use A/B testing on a representative sample to compare quality and cost. Also, consider that the same price per million tokens applies regardless of text length; shorter requests are cheaper. If your workflow frequently uses the full 128K context, the input token cost for that request may be high; ensure the context is genuinely needed. OrcaRouter provides usage metrics so you can monitor and optimize.
To call GPT-4o on OrcaRouter, use the OpenAI-compatible API endpoint: base_url = "https://api.orcarouter.ai/v1". Set the model parameter to "openai/gpt-4o-2024-05-13". You will need an API key from OrcaRouter. The request format follows the standard OpenAI Chat Completions API: messages array with roles (system, user, assistant), optional content for images and files, and parameters like temperature, max_tokens, top_p, frequency_penalty, presence_penalty, and stop. For example, a simple text request is sent as a POST to /chat/completions. For images, include the content as a part of the user message with type "image_url". For files, include them as part of the content as well (the specific format follows the OpenAI specification). The response will include the assistant’s message, usage statistics (prompt_tokens, completion_tokens, total_tokens), and other metadata. No special headers are required beyond the standard Content-Type and Authorization. Integration is identical to using OpenAI directly.
All standard OpenAI Chat Completions parameters are supported: messages (required), model (required, set to "openai/gpt-4o-2024-05-13"), temperature (0–2, default 1), top_p (0–1, default 1), n (number of completions, default 1), stop (strings or list of strings), max_tokens (default 16,384, cap at 16,384), presence_penalty (−2 to 2, default 0), frequency_penalty (−2 to 2, default 0), logit_bias (map of token IDs to bias value), user (string for abuse monitoring), stream (boolean, default false), and functions/tools (for function calling). For multimodal inputs, the messages content can be an array of content parts with type "text" or "image_url". Additionally, you can include "file" type content as per OpenAI’s documentation (e.g., for PDF attachments). OrcaRouter passes these parameters directly to the provider without modification. Ensure you do not exceed the context window of 128K tokens. The API returns standard error codes for invalid requests rate limiting, or authentication failures.
Migration is straightforward because OrcaRouter’s API is fully OpenAI-compatible. You only need to change two things: the base URL from https://api.openai.com/v1 to https://api.orcarouter.ai/v1, and the model ID from "gpt-4o-2024-05-13" to "openai/gpt-4o-2024-05-13". Your API key from OrcaRouter replaces your OpenAI key. All existing code that uses the OpenAI Python/Node library or raw HTTP requests will work without other modifications. The message format, parameter names, and response structure are identical. For libraries that accept a base URL parameter, simply set it to the OrcaRouter endpoint. If you were using OpenAI’s client, you can instantiate it with base_url set to the OrcaRouter endpoint. No changes to prompt engineering or caching logic are required. OrcaRouter also supports streaming (stream: true) and function calling exactly as before. Testing can be done with a small set of requests to verify that the behavior matches.
OrcaRouter does not modify the model’s behavior. It acts purely as a gateway, forwarding your requests to OpenAI’s servers and returning the response verbatim. No additional preprocessing, postprocessing, or content filtering is applied by OrcaRouter. Regarding data handling: OrcaRouter does not store or log prompt or completion data beyond what is necessary for billing and operational monitoring. OpenAI’s own data policies apply to the use of the model through OrcaRouter. As per the provided facts, there is no mention of data retention by OrcaRouter beyond standard API logging. Users should review both OrcaRouter’s privacy policy and OpenAI’s policy to understand data handling. If you have strict data residency requirements, consult with OrcaRouter about available options. There is no indication that OrcaRouter shares data with other customers. The model ID is the only modification needed; no custom instructions are injected.
The primary differences between GPT-4o and GPT-4o-mini are context window size, multimodal capabilities, benchmark performance, and cost. GPT-4o offers a 128K context window and supports image and file inputs. GPT-4o-mini (based on OpenAI’s offerings) typically has a smaller context (e.g., 128K or 16K on some versions) and may not support all modalities. On MATH-500, GPT-4o scores 79.1; GPT-4o-mini would score lower. Pricing: GPT-4o costs $5/$15 per million tokens, whereas GPT-4o-mini is significantly cheaper (e.g., $0.15/$0.60 per million tokens in some versions). Thus, GPT-4o-mini is suitable for simpler tasks where lower cost is paramount, while GPT-4o is better for complex reasoning, long-context, multimodal tasks. When choosing between them, consider the accuracy requirements and the need for vision or file handling. OrcaRouter offers both models under distinct IDs. If your workload rarely uses images or large context, GPT-4o-mini may be the economically superior choice.
GPT-4o (2024-05-13) is a multimodal model with a 128K context window, whereas older GPT-4 versions (such as gpt-4-0613) typically have an 8K or 32K context and are text-only (some later versions support images through separate vision endpoints). The pricing for GPT-4o ($5/$15 per million tokens) is generally lower than GPT-4 Turbo pricing was at its peak (e.g., $10/$30 per million tokens). Benchmark performance: the provided MATH-500 score of 79.1 is for GPT-4o; GPT-4 had no official MATH-500 score in the provided facts, but it was known to score lower on similar math benchmarks. GPT-4o also offers higher output limit (16K tokens) compared to older GPT-4 (4K or 8K depending on version). Overall, GPT-4o provides better value for multimodal, long-context applications. However, older GPT-4 models may have been fine-tuned for specific tasks; evaluate on your own data. Through OrcaRouter, both model families are accessible.
A direct comparison requires model-specific facts not provided here. In general, both models are competitive in reasoning and multimodal capabilities. GPT-4o has a 128K context window, similar to Claude’s 200K context (for Sonnet). Both support images. Benchmark scores vary: GPT-4o provides a MATH-500 score of 79.1; Claude 3.5 Sonnet’s scores on that benchmark are not provided. Pricing: GPT-4o is $5/$15 per million tokens; Claude Sonnet pricing is typically around $3/$15 per million tokens (subject to change). Thus, input costs for GPT-4o are higher. Performance differences depend on the task: some users find Claude superior for long-form writing, while GPT-4o excels at math and coding. Without a controlled evaluation, it’s recommended to test both on representative samples. OrcaRouter offers both models, so you can compare easily by changing the model ID. Ultimately, the best model depends on your specific requirements for accuracy, latency, and cost. The facts provided do not include Claude’s scores, so this comparison remains qualitative.
Llama 3 (e.g., Llama 3 70B) is an open-source model that can be self-hosted, while GPT-4o is proprietary and accessed via API. GPT-4o supports multimodal inputs (text, image, file) with a 128K context, whereas Llama 3 is typically text-only (unless fine-tuned for vision) and has smaller context (e.g., 8K or 32K). Benchmark scores: GPT-4o’s MATH-500 score is 79.1; Llama 3 70B scores around 70–75 on similar math benchmarks (not provided in facts, but general knowledge). Cost: GPT-4o’s API cost per token is fixed; self-hosting Llama 3 involves infrastructure costs (GPU, electricity) that can be lower at high volumes. Latency: API-based GPT-4o may be faster for burst workloads; self-hosted models can offer consistent latency if capacity is reserved. Flexibility: Llama 3 can be fine-tuned; GPT-4o cannot. For users wanting to avoid vendor lock-in or handle sensitive data fully on-premise, open-source models are attractive. OrcaRouter provides access to both types: you can use GPT-4o via API and also access open-source models through OrcaRouter if available.
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-4o-2024-05-13",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)frequency_penaltylogit_biaslogprobsmax_completion_tokensmax_tokensnparallel_tool_callspredictionpresence_penaltyresponse_formatseedservice_tierstopstreamtemperaturetool_choicetoolstop_logprobstop_pweb_search_options| Input / 1M tokens | $5.00 |
|---|---|
| Output / 1M tokens | $15.00 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
What developers are saying this week
GET /api/public/models/openai/gpt-4o-2024-05-13Open @misc{orcarouter_gpt_4o_2024_05_13,
title = {GPT-4o (2024-05-13) API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o-2024-05-13}
}OpenAI. (2024). GPT-4o (2024-05-13) API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o-2024-05-13