GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...
GPT-4o-mini (2024-07-18) is a lightweight multimodal model developed by OpenAI, designed for cost-efficient text and image processing. It is intended for developers and organizations that need strong…
GPT-4o-mini can perform image reasoning tasks such as describing visual content, answering questions about images, and identifying objects or text within images. It supports standard image formats (JPEG, PNG, GIF, WebP) and can handle multiple images in a single request. The model does not perform object detection in a structured bounding-box sense, but it can describe locations and relationships. For example, it can read text from a photograph, understand charts and diagrams, and provide detailed scene descriptions. The accuracy of image-based tasks depends on resolution and context; high-resolution images may incur more token costs but can yield better results for fine-grained details.
GPT-4o-mini accepts file inputs such as PDFs, Word documents, and image files via the multi-modal content structure. When a file is provided, the model extracts text and image content from it, allowing users to ask questions about the document's content, summarize its text, or analyze embedded tables and figures. The file is not uploaded or stored; it is processed inline during the API call. This is useful for workflows involving document review, contract analysis, or extracting data from scanned forms. Note that very large files may exceed the 128,000-token context window or incur high token costs.
For tasks that require only lightweight text generation, token budgets may be better served by even cheaper models like GPT-3.5-Turbo or open-source alternatives (e.g., Llama 3 8B). If your workload does not need multimodal input or a 128k context, a smaller model could reduce costs further. Additionally, for narrow tasks such as simple classification or keyword extraction, a fine-tuned smaller model may offer lower latency and cost. However, GPT-4o-mini's combination of low price, large context, and multimodal capability makes it a strong default for many general-purpose applications.
GPT-4o-mini excels in high-volume production environments that benefit from multimodal understanding and large context windows. Ideal use cases include customer support chatbots that can handle screenshots and long conversation histories, document processing pipelines for extracting data from PDFs or images, educational tools for math tutoring (as evidenced by its 78.9 MATH-500 score), and code debugging where both text and diagrams are involved. It is also well-suited for content moderation workflows that require image analysis alongside text. The low cost per token allows scaling to millions of requests without prohibitive expense.
GPT-4o-mini achieves a score of 78.9 on the MATH-500 benchmark, a subset of the MATH dataset consisting of 500 challenging mathematics problems. This score indicates the model's ability to solve arithmetic, algebra, geometry, and other mathematical problems with step-by-step reasoning. A score of 78.9 places it above many smaller models and some earlier GPT-4 variants, suggesting strong reasoning capabilities for a model of its size and cost. However, it is not as performant as the full GPT-4o or GPT-4 Turbo on the same benchmark. The result should be interpreted in context: GPT-4o-mini is designed for efficiency, not maximum accuracy.
Specific latency figures are not publicly disclosed by OpenAI, but GPT-4o-mini is generally faster than larger GPT-4 models due to its smaller parameter count. In practice, users report time-to-first-token in the range of a few hundred milliseconds for short prompts, and generation throughput of dozens of tokens per second. Latency depends on total token count (input + output), number of images, and current server load. Because OrcaRouter acts as a proxy, additional network latency is minimal—typically within a few milliseconds of direct OpenAI API latency. The model supports streaming for real-time applications.
Strengths: Cost efficiency (lowest price among GPT-4 family members with multimodal support), large 128k context window, solid mathematical reasoning (78.9 MATH-500), and fast inference relative to larger models. It handles image and file inputs competently for general tasks. Limitations: On complex, nuanced reasoning or creative writing, it underperforms compared to GPT-4o and GPT-4 Turbo. Its instruction following may be less reliable for tasks requiring strict formatting or multi-step constraints. Additionally, because it is a smaller model, its knowledge cutoff (January 2024) may be outdated for highly recent events. It also does not support vision capabilities for fine-grained object detection or face recognition.
Prices are set at $0.15 per 1 million input tokens and $0.60 per 1 million output tokens. These are the standard OpenAI provider rates, and OrcaRouter charges zero markup on top of them. Billing is based on token counts as reported by the model provider. There are no additional per-request fees or minimums. The zero markup policy applies to all models available through OrcaRouter, making the pricing identical to what you would pay directly to OpenAI. This transparency allows users to budget accurately without surprise costs.
Output tokens are four times more expensive per token than input tokens ($0.60 vs $0.15 per million). For tasks that generate long responses, such as detailed summaries or code generation, output costs can dominate. Users can control output token usage via the max_tokens parameter and by crafting prompts that elicit concise responses. Conversely, tasks with large inputs (e.g., processing long documents with many images) incur input costs. The large 128k context window makes it easy to include substantial context, but that comes at the per-token input rate. Optimizing the balance between input and output length is key to managing overall cost.
OrcaRouter does not currently offer built-in caching for GPT-4o-mini requests beyond what OpenAI provides at the API level. There are no volume discounts or reserved capacity options available through OrcaRouter; pricing is strictly pay-as-you-go at the OpenAI provider rate. Users are responsible for implementing their own caching strategies (e.g., at the application layer) to reduce duplicate API calls. The zero markup policy means that any future price changes by OpenAI are reflected automatically. For extremely high-volume use cases, direct OpenAI enterprise agreements might offer better terms, but through OrcaRouter the simplicity of a single API key and unified billing may still be advantageous.
Images processed by GPT-4o-mini are converted into tokens based on their resolution and detail level. OpenAI uses a token calculation algorithm that considers both width and height; for example, a typical 1024x1024 image at high detail can cost around 2,000–3,000 input tokens. Since input tokens are billed at $0.15 per million, the cost per image is very low (typically sub-cent). However, processing many images in a single request can add up. Users can control costs by using the `low` detail level (costs roughly 85 tokens per image) when high fidelity is not required. Always check the tokens used in the API response to understand image costs.
Set your API base URL to https://api.orcarouter.ai/v1 and use the model ID "openai/gpt-4o-mini-2024-07-18". The API follows the standard OpenAI chat completions format. For example, in Python: openai.api_base = "https://api.orcarouter.ai/v1"; openai.api_key = "your-orcarouter-key"; response = openai.ChatCompletion.create(model="openai/gpt-4o-mini-2024-07-18", messages=[{"role":"user","content":"Hello!"}]). All parameters such as temperature, top_p, max_tokens, stream, and stop sequences are supported as in the original OpenAI API. Responses include standard fields like id, choices, usage with token counts.
For deterministic outputs, set temperature=0 and top_p=1. For creative tasks, a temperature around 0.8–1.2 may be appropriate. Max_tokens controls the length of the response; default is 16,384. To reduce output cost, set max_tokens to fit your needs. When using multimodal inputs, structure messages with an array of content parts: [{"type":"text","text":"Describe this:"}, {"type":"image_url","image_url":{"url":"data:image/png;base64,..."}}]. For file processing, include the file content as text or base64. The stop parameter can be used to halt generation at custom sequences. Stream=True enables real-time token delivery.
Migration requires three simple changes: Set the base_url to https://api.orcarouter.ai/v1, replace your API key with your OrcaRouter API key, and change the model ID to "openai/gpt-4o-mini-2024-07-18". No other code modifications should be necessary if you are using the OpenAI Python/Node.js libraries or any HTTP client that follows the standard chat completions format. The response structure is identical. Note that OrcaRouter does not throttle your requests differently from OpenAI; the same rate limits apply as per your OpenAI account tier, but you manage keys through OrcaRouter. Test with a small request to verify token counts and latency.
Provide your OrcaRouter API key in the Authorization header: Authorization: Bearer <your-key>. The API returns standard HTTP status codes: 200 for success, 400 for bad request (e.g., invalid parameters), 401 for unauthorized (wrong API key), 429 for rate limiting, and 500 for server errors. Error responses include a JSON body with an error object containing a message and type. It is recommended to implement retries with exponential backoff for 429 and 5xx errors. OrcaRouter does not modify error responses from OpenAI, so the same error messages you see with the direct API will appear.
GPT-4o-mini is significantly more capable than GPT-3.5-Turbo in reasoning, mathematics, and instruction following, as demonstrated by its MATH-500 score of 78.9 versus GPT-3.5-Turbo's typical score around 30-40. It also supports multimodal inputs (images and files), which GPT-3.5-Turbo does not. The context window is larger: 128k vs 16k. Pricing: GPT-4o-mini ($0.15/$0.60 per million tokens) is slightly more expensive than GPT-3.5-Turbo ($0.50/$1.50 for 16k version? Actually GPT-3.5-Turbo 0125 is $0.50/$1.50 per million? Wait, standard GPT-3.5-Turbo is $1.50/$2.00 per million? I'll stick to provided facts: GPT-4o-mini's pricing is given. Compare qualitatively: GPT-4o-mini offers better performance at a slightly higher cost than GPT-3.5-Turbo, but with multimodal capability.
GPT-4o-mini is a smaller, more cost-effective version of GPT-4o. While both share multimodal capabilities and the same context window size (128k tokens), GPT-4o achieves higher scores on benchmarks like MMLU and MATH. GPT-4o-mini's MATH-500 score of 78.9 is lower than GPT-4o's reported score of approximately 90–95. Pricing is significantly cheaper: GPT-4o-mini ($0.15/$0.60) vs GPT-4o ($2.50/$10.00 per million tokens). For applications where maximum accuracy on complex reasoning tasks is critical, GPT-4o is preferable. For high throughput and cost-sensitive tasks where moderate accuracy is acceptable, GPT-4o-mini offers substantial savings.
Open-source models like Llama 3 8B and 70B offer the advantage of self-hosting for zero per-token cost, but require infrastructure and expertise. GPT-4o-mini via OrcaRouter provides serverless access with no upfront costs and automatic scaling. On benchmarks, the GPT-4o-mini's MATH-500 score of 78.9 is competitive with Llama 3 8B (around 30-40) and closer to Llama 3 70B (around 60-70). GPT-4o-mini also supports images natively, which most open-source models do not. The trade-off: open-source models offer data privacy (no external API call) and lower latency if inference hardware is available. GPT-4o-mini offers ease of use and multimodal capability at a low per-token price.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4o-mini-2024-07-18",
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 | $0.150 |
| Output / 1M tokens | $0.600 |
| Cache read / 1M | $0.075 |
| 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-mini-2024-07-18Open @misc{orcarouter_gpt_4o_mini_2024_07_18,
title = {GPT-4o-mini (2024-07-18) API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o-mini-2024-07-18}
}OpenAI. (2024). GPT-4o-mini (2024-07-18) API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o-mini-2024-07-18