GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost....
GPT-5 Mini is a language model by OpenAI designed for tasks that benefit from a large context window and strong mathematical reasoning. With 400,000 tokens of context and 128,000 tokens for output,…
GPT-5 Mini demonstrates strong mathematical reasoning, as indicated by its 90.7 score on the AA Math benchmark. This suggests it is well-suited for problems that require step-by-step calculation, symbolic manipulation, or logical deduction. The large context window allows it to maintain intermediate results over many steps, making it effective for multi-step word problems, theorem derivation, and structured proofs. However, reasoning in other domains (e.g., commonsense, scientific) is not specifically benchmarked in the provided data. The model's capabilities are likely comparable to OpenAI's other models for tasks not requiring the full breadth of GPT-5, but users should evaluate on their own datasets.
GPT-5 Mini can process text, images, and files within a single request. When an image or file is provided, the model interprets it as part of the input context, converting visual or structured data into tokens that count toward the 400,000-token limit. This allows the model to answer questions about images, summarize document contents, or extract data from tables. The exact token cost for images and files is determined by OpenAI's encoding—typically images are divided into tiles, each consuming a fixed number of tokens. For files, the content is often extracted as text. There is no separate charge for modality other than the per-token input pricing.
GPT-5 Mini costs $0.25/M input and $2.00/M output, which is higher than many smaller models (e.g., GPT-4o Mini, Claude Haiku) but lower than the full GPT-5. It is worth choosing when your task demands strong mathematical reasoning (e.g., 90.7 AA Math) and a large context window (400K tokens). If your task is simple classification or short-form generation, a cheaper model may suffice. Also, if you require extremely high output volume, the $2.00/M output cost may accumulate. Consider GPT-5 Mini when you need reliable reasoning over long contexts—like analyzing a 200-page research report—and can benefit from its balance of performance and cost.
GPT-5 Mini's ability to accept images and files makes it suitable for tasks like invoice processing, chart interpretation, and document Q&A. For example, you can upload a PDF of a financial report (file) along with a chart image and ask the model to extract key figures. The 400K context can accommodate lengthy reports with embedded images. Another use is code file analysis: you can provide a repository as a file and request a review. However, note that large images or many files can quickly consume tokens. The model treats all inputs as tokens, so plan to stay within the 400K context limit.
AA Math is a benchmark designed to evaluate a model’s ability to solve mathematical problems across various difficulty levels, often covering algebra, arithmetic, geometry, and logic. A score of 90.7 indicates that GPT-5 Mini correctly answered 90.7% of the test questions. This is a strong result, placing it in the upper tier for mathematical reasoning among models of its size. It suggests that the model can reliably handle quantitative word problems, calculations, and multi-step reasoning. No other benchmarks are provided, so this score should be considered a key strength but not the full picture.
Speed (latency) and throughput are not specified in the provided data. Generally, model speed depends on the provider's infrastructure, request size, and model size. As a 'Mini' variant, GPT-5 Mini is likely optimized for faster inference than the full GPT-5, but exact numbers are unknown. For real-time applications, you should test with typical prompt lengths. OrcaRouter’s API may offer additional performance details. The large output limit (128K tokens) suggests that very long generations will take proportionally longer. Consider batching or streaming for latency-sensitive use cases.
Strengths: Strong mathematical reasoning (90.7 AA Math), large context (400K tokens), multimodal (text, image, file), and reasonable per-token cost for its capability. Limitations: No benchmark scores provided for non-math domains (e.g., language understanding, coding, commonsense). The model may underperform on creative writing or open-ended generation compared to larger alternatives. Also, the 400K context window, while large, is smaller than some frontier models (e.g., Gemini 1.5 Pro has 1M). For tasks requiring extremely long context or very high output token volume, the full GPT-5 or other models might be better.
Pricing is $0.25 per 1 million input tokens and $2.00 per 1 million output tokens. Input tokens include all text, image tokens (after encoding), and file tokens. Output tokens are the generated text. Billing is based on the actual number of tokens used, with no minimum. For example, a request with 10,000 input tokens and 5,000 output tokens costs (10,000/1M) * 0.25 + (5,000/1M) * 2.00 = 0.0025 + 0.01 = $0.0125. This is billed at the provider rate with zero markup by OrcaRouter. There are no additional platform fees or setup costs.
Compared to the full GPT-5 (if available), GPT-5 Mini is likely cheaper per token—$0.25 vs maybe $1+ input. However, it may have reduced breadth. For tasks that only need moderate context or reasoning, using a cheaper model like GPT-4o Mini ($0.15 input) could save money. The trade-off is that GPT-5 Mini offers 400K context and strong math benchmarks. For high-volume applications, even small per-token differences add up. You should estimate your average prompt and generation size. If images or files are common, the token cost from modality will also factor in.
The provided information does not mention any caching features or discounts through OrcaRouter. Pricing is strictly at the provider's rate with zero markup. Some model providers offer cached input discounts (e.g., for repeated prompts), but it is not specified whether OpenAI or OrcaRouter extend such options for GPT-5 Mini. To minimize costs, you can optimize prompt length, reuse system prompts, and avoid unnecessary token usage from large files or images. OrcaRouter may offer caching in the future, but as of now, assume per-token pay-as-you-go pricing.
OrcaRouter provides an OpenAI-compatible API endpoint at base_url https://api.orcarouter.ai/v1. To use GPT-5 Mini, set the model parameter to "openai/gpt-5-mini". The request format is identical to OpenAI’s chat completions API. For example: POST /v1/chat/completions with headers containing your OrcaRouter API key. In the JSON body, include "model": "openai/gpt-5-mini", "messages": [...], and optional parameters like max_tokens, temperature, etc. Responses follow the same structure as OpenAI. This makes migration from direct OpenAI calls straightforward—just change the base URL and model name.
The API supports standard chat completion parameters: messages (array of role-content objects), max_tokens (up to 128000), temperature, top_p, n, stop, frequency_penalty, presence_penalty, and stream (boolean for streaming). For multimodal inputs, include content parts with type "text" and "image_url" or "file_url". The exact naming for file input is not specified, but you can use OpenAI’s standard file or data URI format. Parameters like logprobs, tools, or tool_choice might be supported but are not confirmed by the provided data. Refer to OpenAI's documentation for full parameter details.
Migration typically requires two changes in your code: 1) Change the base URL to https://api.orcarouter.ai/v1. 2) Replace the model ID with "openai/gpt-5-mini". Your existing API client (e.g., the openai Python library) can be configured by setting api_base or base_url. Your API key for OrcaRouter must be used instead of the OpenAI key. All other request parameters and response handling remain the same, since the API is OpenAI-compatible. There is no need to modify message formats or response parsers. For streaming, use the same stream=True pattern.
The API inherits OpenAI’s rate limits and availability, though OrcaRouter may impose its own quotas. The context window limit of 400K tokens is per request. The max output of 128K tokens is also per request. There is no mention of function calling or plugin support. For file uploads, there may be size limits not specified. The API does not support custom model fine-tuning or advanced features beyond what OpenAI offers for GPT-5 Mini. You should check the OrcaRouter documentation for any specific usage policies or throttling.
GPT-5 Mini (context 400K, AA Math 90.7) vs GPT-4o (context typically 128K, AA Math score not provided). GPT-5 Mini has a larger context window and likely better mathematical reasoning. However, GPT-4o may have broader capabilities in creativity, vision understanding, and multilingual tasks. GPT-4o pricing is similar or slightly higher on input ($2.50) and output ($10) per million tokens as of last update. GPT-5 Mini is cheaper on both fronts. For tasks requiring long context and math, GPT-5 Mini is advantageous. For general-purpose chatbot or creative tasks, GPT-4o might be more suitable.
Claude 3.5 Sonnet offers a 200K context window and is strong on reasoning but does not provide an AA Math score. Pricing for Claude 3.5 Sonnet is $3.00 input and $15.00 output per million tokens, substantially higher than GPT-5 Mini. GPT-5 Mini has twice the context and lower cost. However, Claude may excel in safety, refusal handling, and long document summarization. For benchmark-focused math tasks, GPT-5 Mini's 90.7 is a direct advantage. Users who need high-volume, cost-sensitive deployment may prefer GPT-5 Mini.
Smaller models like GPT-4o Mini (128K context, ~$0.15 input, ~$0.60 output) or Gemini 1.5 Flash are cheaper per token and may have lower latency. If your task does not require the 400K context or the mathematical precision of AA Math 90.7, these models can reduce costs. For instance, simple classification, short summaries, or basic Q&A often work well with smaller models. GPT-5 Mini is best reserved for jobs where the larger context and strong math reasoning actually affect output quality. Larger models like GPT-5 or Claude Opus may be needed for extremely nuanced tasks.
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-5-mini",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_completion_tokensmax_tokensreasoningresponse_formatseedstreamstructured_outputstool_choicetools| Input / 1M tokens | $0.250 |
|---|---|
| Output / 1M tokens | $2.00 |
| Cache read / 1M | $0.025 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
Compare monthly costs across models in the LLM cost calculator
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GET /api/public/models/openai/gpt-5-miniOpen @misc{orcarouter_gpt_5_mini,
title = {GPT-5 Mini API},
author = {OpenAI},
year = {2025},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/openai/gpt-5-mini}
}OpenAI. (2025). GPT-5 Mini API. OrcaRouter. https://www.orcarouter.ai/models/openai/gpt-5-mini