The 2024-08-06 version of GPT-4o offers improved performance in structured outputs, with the ability to supply a JSON schema in the respone_format. Read more [here](https://openai.com/index/introducing-structured-outputs-in-the-api/). GPT-4o ("o" for "omni") is...
GPT-4o (2024-08-06) is a version of OpenAI's GPT-4o model, released in August 2024. It is a multimodal large language model that processes text, image, and file inputs. The model is accessed through…
The model's core strengths lie in mathematical reasoning and multimodal understanding. Its MATH-500 score of 79.5 indicates strong ability to solve complex math problems step by step. It can interpret images, diagrams, and charts, providing accurate descriptions or analyses. The 128K context window allows it to process large amounts of information, such as a full-length book or a series of images with associated text. It also handles file uploads, making it useful for tasks like extracting data from spreadsheets or reading code files. These capabilities are backed by OpenAI's continuous optimization and the model is widely used in production environments.
GPT-4o (2024-08-06) excels in tasks that require combining visual and textual information. Examples include solving math problems with diagrams, analyzing screenshots of user interfaces for bug reports, extracting data from scanned documents, and providing detailed explanations of complex figures. It is also effective for code generation and debugging when context includes screenshots of errors or file structures. Educational applications benefit from its step-by-step reasoning. For tasks that are purely text-based and simple, such as basic Q&A or summarization of short texts, a cheaper model like GPT-4o mini may suffice. This model is best reserved for high-value, multimodal, or reasoning-intensive workflows.
For tasks that do not leverage vision, file processing, or advanced reasoning, a smaller and cheaper model is more cost-effective. For example, simple chat applications, short-form content generation, or classification tasks can be handled by models like GPT-4o mini or GPT-3.5 Turbo. If your application processes only text and does not require the 128K context window, a smaller context model may reduce latency and cost. Additionally, if your data does not involve images or files, a text-only model is sufficient. Evaluate whether the complexity of the task justifies the higher per-token price of GPT-4o. OrcaRouter offers a range of models with different price points to match various use cases.
Yes, the model accepts file inputs. You can upload files such as PDFs, Word documents, spreadsheets, images, and code files. The model extracts and understands the content within these files, treating them as part of the context. For example, it can read text from a PDF, interpret numbers in a CSV, or analyze source code in a .py file. When combined with image inputs, it can process mixed media like a document with embedded charts. This is particularly useful for automating data extraction, document review, and code analysis. Note that file processing counts toward the 128,000-token context window, so large files may consume significant space.
On the MATH-500 benchmark, GPT-4o (2024-08-06) achieved a score of 79.5. MATH-500 is a subset of the MATH dataset, consisting of 500 challenging competition-level math problems covering topics like algebra, geometry, number theory, and probability. A score of 79.5 indicates correct answers on approximately four out of five problems. This places it among the top-performing models for mathematical reasoning. While no other benchmark scores are provided, this metric demonstrates the model's strength in step-by-step logical deduction and arithmetic. Users can expect reliable performance on math-heavy applications such as tutoring, verification, and problem generation.
Specific benchmark scores beyond MATH-500 are not provided in the available facts. However, GPT-4o as a model family is widely known to perform competitively on a range of standard benchmarks including MMLU (massive multitask language understanding), HumanEval (code generation), and various vision-language tasks. The August 2024 version may incorporate updates that improve reasoning and instruction following. Without exact figures, users should refer to OpenAI's published evaluations for the most current data. For practical purposes, the model is considered state-of-the-art among open-API models in terms of combined text and vision capabilities.
No specific latency or throughput figures are provided for this model. Generally, GPT-4o is designed to be faster than earlier GPT-4 variants while maintaining quality. Latency depends on factors such as input length, output length, concurrent requests, and the backend infrastructure. When accessed through OrcaRouter, requests are routed to OpenAI's servers, and response times are similar to direct API calls. Users can optimize by using the smallest context necessary and setting reasonable max_tokens limits. For real-time applications, it is advisable to test with representative workloads. OrcaRouter does not introduce additional latency beyond network routing.
Strengths: strong mathematical reasoning (MATH-500 79.5), multimodal input (text, images, files), large 128K context window, and high output token limit (16,384). It is particularly effective for complex reasoning, vision understanding, and tasks requiring long-range context. Limitations: higher cost compared to smaller models; may not be optimal for simple or low-latency tasks; accuracy can vary on ambiguous or poorly formatted inputs; vision capabilities may not perform well on distorted or low-resolution images. Also, as with all LLMs, it can produce plausible-sounding but incorrect answers, especially in domains outside its training data. Mitigate by validating outputs and using prompt engineering.
Pricing for GPT-4o (2024-08-06) is based on token usage. Input tokens are billed at $2.50 per 1 million tokens. Output tokens are billed at $10.00 per 1 million tokens. This is the provider (OpenAI) rate, and OrcaRouter applies zero markup—you pay exactly the same as if you called OpenAI directly. Tokens are counted based on the text sent and received. Image and file inputs consume tokens proportional to their size and processing complexity (image token cost is per OpenAI's policy). There are no additional fees for using the API endpoint. Billing is typically aggregated per usage period, and you can monitor token consumption through OrcaRouter's dashboard.
No hidden costs or surcharges. OrcaRouter bills at the exact provider rate with zero markup. The prices listed ($2.50/1M input, $10/1M output) are all-inclusive. There are no additional fees for authentication, connection, or support. However, image and file inputs incur token costs determined by OpenAI's pricing policies, which may exceed the text-only token cost. For instance, a 1080x1080 image might cost a certain number of tokens beyond the text tokens. Check OpenAI's documentation for exact image token pricing. OrcaRouter passes through these costs without markup. There is no caching-based discount as of the current information.
Choosing GPT-4o (2024-08-06) over cheaper models like GPT-4o mini or GPT-3.5 Turbo involves a trade-off between performance and cost. The per-token price is higher, so for high-volume applications, costs can accumulate quickly. However, if the task requires the model's multimodal capabilities or the 128K context window, cheaper models may not suffice, potentially leading to incomplete or lower-quality outputs. For text-only tasks with simple reasoning, a cheaper model reduces expenses without sacrificing much quality. OrcaRouter enables you to switch between models easily, so you can experiment to find the best cost-performance balance for each use case.
The available facts do not mention any caching or discount mechanisms for this model. OrcaRouter bills at the provider rate without markup, so any discounts would presumably come from OpenAI's own pricing (e.g., tiered discounts for high-volume usage, if applicable). OrcaRouter may offer its own caching features, but this is not confirmed for this model. To minimize costs, consider reducing input token usage by truncating conversation history, using shorter prompts, and limiting output length with the max_tokens parameter. For repeated identical queries, you might implement caching at the application level.
To call GPT-4o (2024-08-06) through OrcaRouter, use the base URL https://api.orcarouter.ai/v1 and set the model ID to "openai/gpt-4o-2024-08-06". The API is fully compatible with OpenAI's client libraries. For example, in Python with the openai library, you would configure openai.api_base = "https://api.orcarouter.ai/v1" and openai.api_key = "your-orcarouter-api-key". Then call openai.ChatCompletion.create(model="openai/gpt-4o-2024-08-06", messages=[...]). All standard parameters (temperature, max_tokens, top_p, etc.) are supported. See the OrcaRouter documentation for details on authentication and rate limits.
Since the model supports the OpenAI-compatible API, you can use the full set of parameters available in OpenAI's chat completions endpoint. Key parameters include: model (set to the model ID), messages (list of role-content objects), temperature (0-2), top_p (0-1), n (number of completions), max_tokens (up to 16384), stop (sequences), frequency_penalty, presence_penalty, logit_bias, and user (for usage tracking). For multimodal inputs, you include an image or file_url in the content of a message using the appropriate format (e.g., content: [{type: "text", text: "..."}, {type: "image_url", image_url: {...}}]). Refer to OpenAI's documentation for image and file input syntax.
Migrating from direct OpenAI API to OrcaRouter requires two changes: 1) Set the base URL to https://api.orcarouter.ai/v1, and 2) Replace your OpenAI API key with an OrcaRouter API key. No code changes are needed for message formatting or parameters. The model ID changes from "gpt-4o-2024-08-06" to "openai/gpt-4o-2024-08-06". All existing logic for handling streaming, function calling, and response parsing remains the same. OrcaRouter's API is designed to be a drop-in replacement. After switching, you can also access other providers' models through the same interface, enabling easy comparison and load balancing.
Authentication is done via an API key provided by OrcaRouter. Include it in the header as "Authorization: Bearer YOUR_ORCAROUTER_API_KEY". Rate limits are managed by OrcaRouter and may differ from OpenAI's direct limits. Check your OrcaRouter plan for details. If you exceed rate limits, you will receive HTTP 429 responses. To mitigate, implement retry logic with exponential backoff. OrcaRouter may also allow you to set per-user rate limits via the user parameter. For high-throughput needs, contact OrcaRouter support for custom arrangements. The model itself does not have separate rate limits; it is subject to the overall endpoint limits.
GPT-4o (2024-08-06) is the full-size flagship model, whereas GPT-4o mini is a smaller, cheaper variant. Key differences: GPT-4o has a 128K context window (vs. GPT-4o mini's 128K also, but the mini has lower output limit typically 16K as well? Actually GPT-4o mini also has 128K context and 16K output, but it is less capable in reasoning and vision. The full model scores 79.5 on MATH-500; GPT-4o mini scores lower on math benchmarks. Pricing: GPT-4o is more expensive ($2.50/$10 vs. GPT-4o mini at $0.15/$0.60 per 1M tokens). For tasks requiring high accuracy or complex reasoning, the full model is warranted. For simpler tasks, the mini provides a cost-effective alternative.
Compared to GPT-4 (e.g., GPT-4-0613 or GPT-4 Turbo), GPT-4o (2024-08-06) offers several improvements: native multimodal input (text, images, files) without requiring separate vision models, larger context window (128K vs. GPT-4's 32K for older versions), and faster inference. The pricing is lower than earlier GPT-4 variants, which were often more costly. For example, GPT-4 Turbo had similar context but higher pricing. In terms of benchmarks, the MATH-500 score of 79.5 outperforms older GPT-4 models. However, some users may find earlier models more stable for specific tasks due to longer training history. Overall, GPT-4o is a more modern and efficient successor.
The choice depends on specific needs and provider preferences. GPT-4o (2024-08-06) excels in math reasoning (MATH-500 79.5) and multimodal tasks with text, images, and files. Claude models from Anthropic may offer stronger safety features or longer context windows in some versions but typically have higher pricing. Gemini models from Google provide multimodal and large context, but may have different performance profiles. Using OrcaRouter, you can test multiple providers with a single API. For tasks requiring high mathematical accuracy, GPT-4o is a strong candidate. For pure text summarization or low-cost tasks, other models may be more economical. Evaluate benchmark scores and pricing for your use case.
When using GPT-4o through OrcaRouter, your data is sent to OpenAI's servers for inference. OrcaRouter does not store or process your prompts beyond forwarding them. OpenAI's data usage policies apply—check OpenAI's terms if you have concerns about training data usage or data retention. If you require data to stay within a specific jurisdiction or avoid certain providers, OrcaRouter allows you to choose among multiple providers per request. You can also use OrcaRouter's routing features to direct requests to providers that meet your compliance requirements. Always review the data handling documentation of both OrcaRouter and the underlying provider.
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-2024-08-06",
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.
GET /api/public/models/openai/gpt-4o-2024-08-06Open @misc{orcarouter_gpt_4o_2024_08_06,
title = {GPT-4o (2024-08-06) API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o-2024-08-06}
}OpenAI. (2024). GPT-4o (2024-08-06) API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o-2024-08-06