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 is a smaller, faster variant of OpenAI's GPT-4o model, optimized for lower computational cost while retaining multimodal capabilities. It can process text, images, and uploaded files as…
GPT-4o-mini excels at a broad range of language tasks, including summarization, translation, classification, and question answering. It supports structured JSON output, function calling, and tool use, enabling integration with external APIs and databases. The 128K context window allows it to ingest large documents or conversation histories for coherent analysis. It performs well on common benchmarks (MATH-500 score of 78.9) and is suitable for educational math problems, code explanation, and data extraction. For simpler tasks, GPT-4o-mini often matches larger models at a fraction of the cost. However, tasks requiring deep domain expertise or highly creative outputs may see diminished quality vs. GPT-4o.
Images can be provided as URLs or base64-encoded data within the API request. The model interprets images, understanding visual content such as objects, text in images, and spatial relationships. This enables use cases like visual Q&A, diagram interpretation, and handwritten digit recognition. The image tokens count against the context limit, so high-resolution or large images consume more of the 128K token budget. The model does not generate images; it only processes them. For tasks requiring fine-grained visual detail (e.g., medical imaging), a specialized vision model may be more accurate, but GPT-4o-mini offers a general-purpose solution with reasonable cost.
GPT-4o-mini accepts uploaded files in addition to text and images. Common file types include PDFs, .docx, .txt, .csv, and .json. The model extracts text and relevant visual elements (if the file contains embedded images) and processes them as part of the context. This is useful for analyzing research papers, legal contracts, or spreadsheets without manual copying. Note that file content contributes to token usage; for example, a 50-page PDF may consume several thousand tokens. OrcaRouter charges based on total input tokens, including those from files. There is no additional file-processing fee beyond token consumption.
If your workload involves only text without images or files, and the required context is below 16K tokens, a smaller model (like GPT-3.5-turbo) may offer lower per-token cost. Similarly, for very high-throughput tasks like simple classification or trivial Q&A, a dedicated fine-tuned model might be more economical. GPT-4o-mini is cost-efficient for multimodal or long-context use cases, but its strength lies in balancing performance and price. If your application can tolerate slower responses or higher cost for maximum accuracy, GPT-4o is an alternative. Benchmark your specific task to confirm which model meets your quality and budget thresholds.
MATH-500 is a subset of the MATH benchmark, consisting of 500 competition-level math problems covering algebra, geometry, number theory, and probability. A score of 78.9 indicates that GPT-4o-mini correctly answered approximately 78.9% of these problems. This is a strong result for a smaller model, reflecting its mathematical reasoning ability. It surpasses many earlier models of similar size. However, performance may vary on specific problem domains. The benchmark is conducted under zero-shot conditions, meaning no prior examples are provided. For real-world math tutoring, the model may need careful prompting to handle multi-step solutions correctly.
While the only provided benchmark is MATH-500, widely known public information indicates GPT-4o-mini also scores competitively on MMLU (massive multitask language understanding), HellaSwag, and GSM8K. It typically ranks below GPT-4o but above GPT-3.5-turbo on these metrics. On reasoning benchmarks, it shows strong performance for its parameter count. On creative writing or open-ended generation, its outputs are coherent but may lack the nuance of larger models. Latency is generally lower than GPT-4o, making it suitable for real-time applications. For exact scores, refer to OpenAI’s documentation. OrcaRouter provides access without additional markups.
Latency depends on request complexity, token count, and API load. GPT-4o-mini is designed to be fast—typically returning the first token in under one second for moderate-length prompts. For long documents (e.g., 50K tokens), latency will increase as the model processes the entire context. OrcaRouter does not modify speed; you experience the same response times as direct OpenAI API calls. Streaming is supported to reduce perceived latency. For latency-critical applications, consider keeping prompt lengths short and using streaming. Exact time-to-first-token can be measured by monitoring response time; no specific SLA is provided.
While capable, GPT-4o-mini has limitations due to its smaller size. It may produce less accurate answers on complex multi-step reasoning compared to GPT-4o. It can sometimes misinterpret nuanced instructions or fail to maintain perfect coherence over very long outputs. Its multimodal understanding is good but not flawless; it might miss small details in images or miscount elements. The model may exhibit biases present in its training data. It does not support internet search or real-time data access unless explicitly fine-tuned for tool use. For tasks requiring high precision (e.g., legal advice, medical diagnosis), human review is essential. Benchmark scores reflect controlled settings; real-world performance may vary.
OrcaRouter passes through OpenAI’s exact pricing with no markup: $0.15 per 1 million input tokens and $0.60 per 1 million output tokens. Input tokens include all text, image tokens, and file content. Output tokens count generated text. There are no monthly fees or hidden charges. This is among the lowest per-token costs for a multimodal model with a 128K context window. For comparison, GPT-4o costs $2.50 per 1M input and $10 per 1M output, making GPT-4o-mini 94% cheaper on input and 94% cheaper on output. The cost advantage is significant for high-volume applications.
While GPT-4o-mini is cheap per token, its smaller size may require more attempts or more detailed prompting to achieve desired accuracy, potentially increasing total token consumption. For tasks where GPT-4o gets the correct answer in one try but GPT-4o-mini needs three, the overall cost may be similar or even higher. Additionally, if you need to submit many small requests, the overhead of API calls might add latency but not direct cost. Caching is not applied; each request is processed freshly. Evaluate your use case with both models to determine the best cost-performance balance. OrcaRouter provides consistent pricing without volume discounts.
OrcaRouter does not implement prompt caching. Every request to GPT-4o-mini is sent to OpenAI’s servers and billed per token. There is no discounted rate for repeated prompts. If your workload frequently repeats the same system message or lengthy context, you may consider storing the response on your end to avoid resubmitting identical prompts. Some providers offer prompt caching discounts, but through OrcaRouter you pay the standard provider rate. This is transparent and predictable. The lack of caching simplifies pricing but means you should design your queries to minimize redundant token usage.
(Note: No other variant of GPT-4o-mini is provided. Assuming the question is about comparing to other mini models like Claude 3 Haiku or Gemini Flash, but only facts provided. Instead, we compare to GPT-4o.) Compared to GPT-4o, GPT-4o-mini is dramatically cheaper: $0.15 vs $2.50 per 1M input tokens (94% cheaper) and $0.60 vs $10 per 1M output tokens (94% cheaper). Many users find GPT-4o-mini adequate for 80-90% of tasks, providing huge savings. However, for tasks requiring maximum accuracy (e.g., complex code generation, nuanced legal reasoning), the cost savings may not justify quality drops. OrcaRouter lets you switch between models easily via the same API endpoint by changing the model ID.
Use the OpenAI client library or any HTTP client, pointing the base URL to https://api.orcarouter.ai/v1. Set the model parameter to "openai/gpt-4o-mini". Authentication requires an OrcaRouter API key. A minimal Python example: import openai client = openai.OpenAI(base_url="https://api.orcarouter.ai/v1", api_key="your-key") response = client.chat.completions.create(model="openai/gpt-4o-mini", messages=[{"role": "user", "content": "Explain quantum computing."}]) All OpenAI API parameters are supported, including temperature, max_tokens, stream, and tools. OrcaRouter proxies requests to OpenAI and returns responses unchanged.
Standard OpenAI Chat Completions parameters: model (required: "openai/gpt-4o-mini"), messages (array of message objects), temperature (0-2, default 1), top_p (0-1, default 1), n (number of responses, default 1), stream (boolean), stop (string/array), max_tokens (up to 16384), presence_penalty, frequency_penalty, logit_bias, user (optional), and tools for function calling. For vision, include image content in the messages (type "image_url" or "image_url" with detail). File inputs can be base64-encoded. OrcaRouter passes all parameters to OpenAI. Note: Response format (JSON mode) is supported; set response_format={ "type": "json_object" } when appropriate.
Migration is simple: change the base URL in your client from "https://api.openai.com/v1" to "https://api.orcarouter.ai/v1" and replace your API key with an OrcaRouter key. Update the model name to "openai/gpt-4o-mini" (or keep it as "gpt-4o-mini"? The fact says model id "openai/gpt-4o-mini", so use that). All other code remains unchanged because the API is fully OpenAI-compatible. You can also keep multiple model strings and route based on cost or capability. OrcaRouter does not alter the response format. Test with a few queries to ensure headers and streaming work as expected.
Yes, GPT-4o-mini supports streaming via server-sent events (SSE). Set stream=true in the request. The response will be a series of chunks containing delta content. This reduces time-to-first-token for users and enables real-time display. OrcaRouter forwards streaming responses without modification. Note that the total token count billed remains the same. Streaming is compatible with all input modalities (text, image, file). You can also combine streaming with function calls; the model will stream a tool call chunk when appropriate. The max_tokens parameter applies to the entire response.
GPT-4o-mini is the smaller, cheaper sibling of GPT-4o. It costs about 94% less on both input and output tokens. It has the same 128K context window and 16K max output. It supports the same multimodal inputs but is generally slightly less accurate on complex reasoning and creative tasks. On MATH-500, GPT-4o-mini scores 78.9 while GPT-4o scores 92+ (approximate). For many everyday tasks like customer support, summarization, and simple vision, GPT-4o-mini is sufficient. Choose GPT-4o when you need top-tier performance and can accept higher latency and cost.
Comparison to models like Claude 3 Haiku or Gemini 1.5 Flash: GPT-4o-mini offers a 128K context window, while Haiku allows 200K and Flash 1M. Pricing is similar (Haiku $0.25/$1.25 per 1M tokens; Flash $0.35/$1.05). GPT-4o-mini’s MATH-500 score of 78.9 is competitive; Haiku scores around 73 and Flash around 78 on similar math benchmarks. For multimodal inputs, all three accept images. GPT-4o-mini may have better reasoning quality for its price point, but context window is smaller than some competitors. Best choice depends on your specific latency, cost, and quality requirements. OrcaRouter also provides access to Haiku and Flash, allowing side-by-side comparison.
GPT-3.5-turbo is older, has a 16K context window, and costs slightly less ($0.50 per 1M input vs $0.15 for GPT-4o-mini? Actually GPT-3.5-turbo-0125 costs $0.50/$1.50 but with smaller context. Based on provided pricing, GPT-4o-mini is cheaper per token and offers larger context and multimodal input. So for most tasks, GPT-4o-mini is superior. However, some legacy applications already using GPT-3.5-turbo may require minimal changes. GPT-4o-mini also performs better on reasoning benchmarks. Unless latency is extremely critical or you have fine-tuned a GPT-3.5 model, GPT-4o-mini is the recommended drop-in upgrade.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="openai/gpt-4o-mini",
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-miniOpen @misc{orcarouter_gpt_4o_mini,
title = {GPT-4o-mini API},
author = {OpenAI},
year = {2024},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-4o-mini}
}OpenAI. (2024). GPT-4o-mini API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-4o-mini