A cost-efficient GPT-5 variant with reasoning controls and a 400K context window for high-volume, well-defined tasks.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where GPT-5 Mini Earns Its Place: Cost-Controlled Reasoning at Scale
Benchmarks
GPT-5 Mini on Independent Benchmarks: Solid Intelligence, Modest Speed
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "gpt-5-mini",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-5-mini",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}'curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-5-mini",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}'
Comparison
GPT-5 Mini vs GPT-5: What You Trade for Lower Cost
GPT-5 Mini and full GPT-5 share the same 400K context window, 128K max output, text-and-image input, reasoning controls, and tool and structured-output support, so switching between them rarely requires re-architecting a request. The real decision is intelligence versus cost. GPT-5 delivers higher benchmark scores and a more recent knowledge cutoff, while Mini occupies the lower-cost tier of the family. Because the API surface is effectively identical, many teams route the same prompts to whichever tier the task actually needs.
Choose GPT-5 Mini when tasks are well-defined, prompts are precise, and volume makes per-token cost the dominant factor—classification, extraction, structured drafting, and routine agent steps. Choose GPT-5 when you need higher reasoning ceilings, stronger coding and agentic performance, or its more recent knowledge, and when a smaller share of premium calls justifies the higher cost. A common pattern is Mini as the default with automatic escalation to GPT-5 for hard cases.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
High-volume classification and extraction
Mini's combination of low input cost, structured-output support, and adequate intelligence fits large-scale labeling, routing, and field extraction from documents. Its 400K context lets you feed long source material in a single call, and JSON-schema outputs make results directly machine-consumable. Use low or minimal reasoning effort for throughput, reserving higher effort for ambiguous items.
Schema-driven content drafting
For SEO briefs, content restructuring, and templated drafting where the output shape is known, Mini produces consistent results at volume cheaper than the flagship. Independent scoring notes it is fairly concise, which helps keep output token cost predictable. Validate against a schema and add fallback routing for edge cases that need stronger reasoning.
Routed agent steps at scale
Function calling plus tool_choice let Mini act as the default worker in an agent pipeline—handling the many routine tool invocations while a larger model handles hard steps. Its shared 400K context holds tool outputs and history well. Watch reasoning latency: keep effort low for interactive agent loops and batch where possible.
Long-document processing with RAG
The 400K window comfortably holds large documents plus retrieved passages, and vision input allows scanned pages and charts alongside text. Because the knowledge cutoff is May 2024, always ground current-information tasks in retrieved context rather than parametric memory, and validate extracted facts before downstream use.