OpenaifastVisionTool use

GPT-5 Image Mini

GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by [GPT-5 Mini](https://openrouter.ai/openai/gpt-5-mini), with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text rendering, and detailed image editing with reduced latency and cost. It excels at high-quality visual creation while maintaining strong text understanding, making it ideal for applications that require both efficient image generation an

400K context·~300K words·128K max output
Context window400Ktokens
Max output128Ktokens

Context window

This model accepts 400K tokens in one request (~300K words of text).

Context window size400K tokens
4K32K128K1M10M

What fits in one request

  • Short document
    About 1,500 words of text
    Fits
  • Long document
    About 37K words of text
    Fits
  • Small codebase
    About 150K words of text
    Fits
  • Full novel
    About 375K words of text
    Won't fit

Specifications

Context size, pricing, and release info in one place.

Context window
400,000 tokens (400K)
Max output tokens
128,000 tokens (128K)
Speed tier
fast
Provider
Openai
Release date
Oct 2025

Capabilities

See which features this model supports, such as vision, tools, and streaming.

Supported (6)
Vision
Supported
Tool use
Supported
Function calling
Supported
Extended thinking
Supported
Streaming
Supported
Prompt caching
Supported
Not supported (2)
Web search
Not supported
Batch API
Not supported

Best for

Jump to a guide or ranking that matches each workload.

Compare GPT-5 Image Mini

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Frequently asked questions

Short answers about context size and how this model behaves.

GPT-5 Image Mini has a context window of 400K tokens (400,000 tokens). This large window is well-suited for long document analysis, extensive codebases, and multi-session agent workflows.

More from Openai

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Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model