OpenaifastVisionTool use

o4 Mini High

OpenAI o4-mini-high is the same model as [o4-mini](/openai/o4-mini) with reasoning_effort set to high. OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains. D

200K context·~150K words·100K max output
Context window200Ktokens
Max output100Ktokens

Context window

This model accepts 200K tokens in one request (~150K words of text).

Context window size200K 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
200,000 tokens (200K)
Max output tokens
100,000 tokens (100K)
Speed tier
fast
Provider
Openai
Release date
Apr 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 o4 Mini High

Open a side-by-side comparison with one click.

Frequently asked questions

Short answers about context size and how this model behaves.

o4 Mini High has a context window of 200K tokens (200,000 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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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
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Key memories
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