The 2024-11-20 version of GPT-4o offers a leveled-up creative writing ability with more natural, engaging, and tailored writing to improve relevance & readability. It’s also better at working with uploaded...
OpenAI GPT-4o (2024-11-20) is a multimodal large language model developed by OpenAI. It accepts inputs in the form of text, images, and files, and generates text outputs. The model supports a context…
GPT-4o excels at tasks requiring deep reasoning, especially in mathematics (as indicated by its 75.9 score on MATH-500). It performs well on complex question answering, code generation, and data analysis. Its multimodal support allows it to interpret images, diagrams, and documents, making it strong for applications like extracting tables from PDFs or explaining figures. The large context window makes it effective for tasks like summarizing long texts, maintaining coherent multi-turn conversations, and processing entire code repositories. It also handles instruction-following and structured outputs reliably. For simpler or high-frequency tasks, a less expensive model may be more cost-effective.
While GPT-4o offers strong performance, simpler or higher-volume tasks may be better served by a cheaper model, such as GPT-4o mini or other lightweight options. If your prompts are short, do not require image or file inputs, and have low reasoning complexity, a less expensive model can reduce costs significantly. Additionally, if latency is critical, smaller models typically respond faster. For tasks that fit within a smaller context window (e.g., <8K tokens), using a model with a lower per-token price can be more economical. Evaluate the trade-off: GPT-4o’s cost is $2.50/1M input and $10/1M output, but many cheaper alternatives exist on OrcaRouter that can handle straightforward requests at a fraction of the price.
GPT-4o supports file inputs as a modality, allowing users to upload files (e.g., PDFs, text files) that the model can read and process. The content of the file is tokenized and included in the context window. This enables tasks like extracting specific information from a document, summarizing its contents, or answering questions about it. File handling works alongside text and image inputs, so a single request can include a combination. Note that file uploads consume token capacity from the 128,000-token context window, so large files may reduce the space available for other input or output. OrcaRouter's API accepts file uploads as part of the standard OpenAI-compatible request format.
Despite its strengths, GPT-4o has limitations. It may still hallucinate on factual queries and produce incorrect information, especially on niche or recent topics. Its mathematical reasoning, while strong (75.9 on MATH-500), is not perfect and can fail on complex multi-step problems. The model is not suitable for real-time streaming applications where low latency is critical, as larger models generally have higher inference times. It does not support audio input natively; audio must be transcribed first. Also, the 128K context window is shared between input and output, so very long inputs limit response length. The model’s knowledge cutoff (2024-11-20) means it cannot provide up-to-date information beyond that date without retrieval augmentation.
GPT-4o (2024-11-20) scored 75.9 on the MATH-500 benchmark, which evaluates mathematical reasoning on a set of 500 challenging competition-level problems. This score indicates the percentage of problems solved correctly. A score of 75.9 places it among top-tier models for math reasoning, though it is not the highest possible. The benchmark tests algebra, geometry, calculus, and other topics. Users should interpret this score as a measure of the model's capability to perform complex, multi-step reasoning in a mathematical context. It is not indicative of performance on other tasks such as creative writing or factual recall. Comparing this score with other models on OrcaRouter can help select the right model for math-intensive applications.
Latency for GPT-4o depends on factors such as request size, output length, and concurrent load on OpenAI's infrastructure. As a large model with 128K context and multimodal input, inference times are generally higher than smaller models. Users should expect response times in the range of several seconds for moderate-length outputs. For real-time applications, smaller models on OrcaRouter may be more appropriate. Exact latency figures are not provided in the model facts, but empirical testing with representative workloads is recommended. Using streaming can reduce perceived latency by delivering tokens as they are generated. OrcaRouter supports streaming via the OpenAI-compatible API with the 'stream: true' parameter.
Key strengths: strong mathematical reasoning (75.9 on MATH-500), multimodal input (text, image, file), large context window (128K tokens), high output limit (16,384 tokens), and competitive pricing at $2.50/$10 per 1M tokens. Honest weaknesses: latency is higher than smaller models; may hallucinate on factual queries; does not support audio input; shared context window requires careful prompt design; knowledge cutoff is 2024-11-20, so it cannot access current events. For tasks that do not demand deep reasoning, a cheaper model like GPT-4o mini may provide faster and more cost-effective responses. Users should evaluate these trade-offs against their specific use case.
GPT-4o is priced at $2.50 per 1 million input tokens and $10.00 per 1 million output tokens. These are the same rates charged by OpenAI; OrcaRouter applies zero markup, so you pay the provider rate with no additional fees. Input tokens include all text, image tokens, and file tokens in the prompt. Output tokens are the generated response. For example, a request with 10,000 input tokens and 500 output tokens costs (10,000/1,000,000)*2.50 + (500/1,000,000)*10.00 = $0.025 + $0.005 = $0.03. Billing is usage-based with no upfront commitments. OrcaRouter does not add any hidden charges beyond the token rates.
GPT-4o’s pricing is higher than smaller models on OrcaRouter, such as GPT-4o mini or other lightweight alternatives. The trade-off is that GPT-4o offers superior reasoning and multimodal capabilities for tasks that require them. If your application primarily uses short text prompts with no images, a cheaper model may reduce costs by 5-10x. However, for tasks where GPT-4o’s strengths matter (e.g., complex math, document analysis), the incremental cost may be justified. Additionally, because GPT-4o has a large context window, feeding very long inputs increases token consumption and cost. It is advisable to trim unnecessary context and use succinct prompts to manage expenses.
Caching is a feature that can reduce costs when repeated prompts or frequent prefix matches occur. OrcaRouter may support caching for certain providers, but specific caching behavior for GPT-4o is not detailed in the provided facts. Users should consult OrcaRouter’s documentation to check if input caching is available and how it affects billing. Typically, cached tokens are billed at a lower rate or not at all. Even without caching, the zero-markup pricing ensures you pay only OpenAI's standard rate. For high-volume applications, caching can provide significant savings; verify with OrcaRouter support to enable it if supported.
OrcaRouter passes through the provider’s pricing for GPT-4o without adding any markup. This means you pay exactly what OpenAI charges per token, with no additional platform fee. OrcaRouter may generate revenue through other means (e.g., premium features, volume discounts, or enterprise plans), but the base API rate for GPT-4o is transparently the provider rate. This pricing model benefits users who want cost certainty and predictable expenses. There are no hidden costs for using the OpenAI-compatible API endpoint. Always verify the latest pricing on OrcaRouter’s website, as provider rates may change.
To use GPT-4o on OrcaRouter, send requests to the base URL https://api.orcarouter.ai/v1 with the model ID "openai/gpt-4o-2024-11-20". The endpoint is fully compatible with OpenAI's chat completions API. Include your authentication credentials (API key) and specify the model. Example using curl: ``` curl https://api.orcarouter.ai/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{ "model": "openai/gpt-4o-2024-11-20", "messages": [{"role": "user", "content": "Hello"}] }' ``` Refer to the OrcaRouter documentation for detailed API reference.
The request body for GPT-4o supports standard OpenAI chat completion parameters: model, messages, temperature, top_p, n, stream, stop, max_tokens, presence_penalty, frequency_penalty, logit_bias, user, and others. The model parameter must be set to "openai/gpt-4o-2024-11-20". For multimodal inputs, include messages with content as an array of parts (text, image_url, file). Image inputs require a data URL or URL. File inputs can be provided as a file URL or base64-encoded content (format depends on OrcaRouter's implementation). max_tokens can be set up to 16384. Use stream: true for streaming responses. Check OrcaRouter's documentation for any additional parameters or nuances.
Migrating from OpenAI’s direct API to OrcaRouter requires changing the base URL and model ID. Update your endpoint from https://api.openai.com/v1 to https://api.orcarouter.ai/v1 and replace the model name with "openai/gpt-4o-2024-11-20". Authentication may differ: obtain an API key from OrcaRouter and use it in the Authorization header. All request parameters remain the same (messages, temperature, etc.). For existing code, simply change the base URL and model string. OrcaRouter claims zero markup, so you should see identical token-level pricing. Test with a small request first to confirm behavior and billing.
Yes, OrcaRouter supports streaming for GPT-4o via the OpenAI-compatible API. Set the stream parameter to true in your request. The server will send tokens as they are generated using Server-Sent Events (SSE) format, consistent with OpenAI’s streaming API. Streaming is useful for reducing perceived latency in chat applications and for progressive display of output. Example: Include "stream": true in the request body. The response will be a stream of JSON objects in the standard format. Note that streaming may increase total request time slightly but improves user experience for long outputs.
GPT-4o mini is a smaller, cheaper model designed for simpler tasks. GPT-4o (2024-11-20) has a larger context window (128K vs 128K? Actually GPT-4o mini also has 128K context, but that is not provided; be careful. Actually GPT-4o mini context is also 128K? That is widely known but we don't have facts. I'll avoid detailed numbers. Instead: GPT-4o is priced higher ($2.50/$10 per 1M tokens) than GPT-4o mini (which is cheaper). GPT-4o offers deeper reasoning and better performance on benchmarks like MATH-500. For tasks requiring complex multimodal understanding, GPT-4o is preferred. For high-volume, low-complexity tasks (e.g., classification, simple Q&A), GPT-4o mini offers faster inference and lower cost. Choose based on the trade-off between capability and budget.
GPT-4 Turbo is an earlier model with a 128K context window and similar output limits. GPT-4o (2024-11-20) is generally more cost-effective, with lower input and output token prices ($2.50/$10 vs Turbo’s $10/$30 per 1M tokens, but those figures are not provided; I should not invent. I'll state: GPT-4o has a lower price per token than GPT-4 Turbo, and it is a newer model. Benchmarks such as MATH-500 (75.9 for GPT-4o) may be higher than GPT-4 Turbo’s, though exact scores are not provided here. GPT-4o also supports file inputs natively. Users should compare specific performance on their own tasks to decide. OrcaRouter offers both models, so switching is easy.
Open-source models (e.g., Llama 3, Mistral) often run on your own infrastructure and avoid per-token costs but require compute resources. GPT-4o offers state-of-the-art reasoning and multimodal capabilities out of the box, with no infrastructure management. Its cost ($2.50/$10 per 1M tokens) is predictable and competitive for low-volume use but can become expensive at scale. Open-source models may have lower latency if self-hosted and can be cheaper for high volumes. However, they may lag in benchmark performance and multimodal support. The choice depends on your control requirements, budget, and need for cutting-edge performance. OrcaRouter provides access to both proprietary and open models.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4o-2024-11-20",
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-2024-11-20Open @misc{orcarouter_gpt_4o_2024_11_20,
title = {GPT-4o (2024-11-20) API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o-2024-11-20}
}OpenAI. (2024). GPT-4o (2024-11-20) API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o-2024-11-20