OpenaiGPT-4o minifastVisionTool use

GPT-4o-mini (2024-07-18)

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 than other recent frontier models, and more than 60% cheaper than [GPT-3.5 Turbo](/models/openai/gpt-3.5-turbo). It maintains SOTA intelligence, while being significantly more cost-effective. GPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferen

128K context·~96K words·16K max output
Context window128Ktokens
Max output16Ktokens

Context window

This model accepts 128K tokens in one request (~96K words of text).

Context window size128K 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
    Won't fit
  • Full novel
    About 375K words of text
    Won't fit

Specifications

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

Context window
128,000 tokens (128K)
Max output tokens
16,384 tokens (16K)
Speed tier
fast
Provider
Openai
Model family
GPT-4o mini
Release date
Jul 2024
Input cost
$0.165/M / 1M tokens
Output cost
$0.660/M / 1M tokens
Cached input
$0.083/M / 1M tokens

Capabilities

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

Supported (7)
Vision
Supported
Tool use
Supported
Function calling
Supported
Web search
Supported
Streaming
Supported
Batch API
Supported
Prompt caching
Supported
Not supported (1)
Extended thinking
Not supported

Best for

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Compare GPT-4o-mini (2024-07-18)

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Frequently asked questions

Short answers about context size and how this model behaves.

GPT-4o-mini (2024-07-18) has a context window of 128K tokens (128,000 tokens). This covers most professional use cases including large code files, lengthy reports, and long conversation histories.

More from Openai

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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

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