What is o4-mini?

o4-mini is OpenAI’s compact o-series reasoning model, released on April 16, 2025, featuring a 200,000-token context window and text-plus-image input for reasoning-heavy tasks, with API pricing of $1.10 per 1M input tokens, $0.275 per 1M cached input tokens, and $4.40 per 1M output tokens as of June 2026.

OpenAI describes o4-mini as a smaller model optimized for fast, reasoning with efficient performance in coding and visual tasks. It is part of the o-series reasoning family and is especially relevant for developers comparing cost, latency, context length, and multimodal input support. Teams already evaluating related OpenAI models such as GPT-4o, GPT-4o mini, and o3 often compare o4-mini when they need reasoning behavior at lower cost than larger reasoning models.

What Are o4-mini’s Key Specifications and Pricing?

The table below uses OpenAI’s official model documentation for provider specifications and pricing, and Gate.AI documentation for Gate.AI API compatibility and access mechanics.

What Can o4-mini Do That Makes It Useful in Production?

o4-mini is useful for reasoning-oriented production workflows where teams need multi-step analysis without always using a larger reasoning model. OpenAI positions it for math, coding, and visual tasks, while its 200K context window helps applications process long instructions, structured records, or multi-document prompts within a single request.

For developer workflows, o4-mini can support code analysis, debugging assistance, function calling, and structured outputs. This makes it relevant for code review assistants, issue triage, data transformation, and agentic workflows that need predictable response formats. It still requires validation, tests, and human review before deployment in production systems.

For multimodal reasoning, o4-mini accepts image input and can produce text output. This can help with chart interpretation, screenshot analysis, document-image review, and visual debugging. However, audio and video are not supported modalities for this model as of June 2026.

For cost-aware workloads, o4-mini may fit high-volume reasoning tasks because its token pricing is lower than o3’s listed pricing. A related alternative such as Gemini 2.0 Flash may also be relevant when teams prioritize different latency, modality, or provider requirements.

What Are o4-mini’s Supported Modalities?

Where Does o4-mini Fall Short?

o4-mini is not a general audio, video, or image-generation model. OpenAI lists text output, text input, and image input, while audio and video are not supported for this model as of June 2026.

Its knowledge cutoff is June 1, 2024, so current events, prices, laws, product availability, and fast-changing technical details require retrieval, browsing, or external data grounding. This is a general AI limitation and is not unique to o4-mini unless stated by the provider.

Like other reasoning models, o4-mini can still produce incorrect answers, unsupported assumptions, or plausible but false explanations. High-stakes legal, medical, financial, security, or compliance usage should include expert review, testing, logging, and safety controls.

OpenAI’s documentation also notes that o4-mini is succeeded by GPT-5 mini. That does not make o4-mini unusable, but teams should review current availability, pricing, deprecation status, and migration options before building long-term systems around it.

What Is o4-mini Best Used For?

How Does o4-mini Compare to o3 and o3-mini?

Comparison values ​​are based on OpenAI model documentation as of June 2026.

How Do I Access o4-mini Through Gate.AI?

Gate.AI provides an OpenAI-compatible API format with the base URL https://api.gate.ai/openai/v1, bearer-token authentication, and a chat completions endpoint at POST /chat/completions. Gate.AI documentation also describes one API key, smart routing, API key creation, pay-as-you-go pricing, API key management, usage insights, and organization permissions.

For this page, the Gate.AI model ID is taken from the user-provided identifier openai/o4-mini. Gate.AI’s public models page was checked, but the rendered search result did not expose a specific o4-mini row. The executable examples below therefore rely on Gate.AI’s verified OpenAI-compatible API details and the user-provided model ID.

Python Example

from openai import OpenAI
import os
client = OpenAI(
    api_key=os.environ["GATEAI_API_KEY"],
    base_url="https://api.gate.ai/openai/v1",
)
response = client.chat.completions.create(
    model="openai/o4-mini",
    messages=[
        {"role": "user", "content": "Explain the difference between cached input and output tokens."}
    ],
)
print(response.choices[0].message.content)

curl Example

curl https://api.gate.ai/openai/v1/chat/completions \
  -H "Authorization: Bearer $GATEAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/o4-mini",
    "messages": [
      {
        "role": "user",
        "content": "Explain the difference between cached input and output tokens."
      }
    ]
  }'

Through Gate.AI, developers can use OpenAI-compatible tooling while managing access through Gate.AI account-level API keys, routing settings, usage insights, and organization controls where those features are enabled in the relevant plan.

FAQs

What is o4-mini’s context window?
o4-mini has a 200,000-token context window as listed in OpenAI’s model documentation as of June 2026.

How much does o4-mini cost?
OpenAI lists o4-mini at $1.10 per 1M input tokens, $0.275 per 1M cached input tokens, and $4.40 per 1M output tokens as of June 2026.

Can User access o4-mini through Gate.AI?
Gate.AI’s OpenAI-compatible API details are verified, and via Gate.AI model ID openai/o4-mini.

What is o4-mini useful for?
o4-mini is suitable for cost-aware reasoning, coding assistance, structured outputs, long-context analysis, and image-input reasoning. It should still be tested and monitored before production use.