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OpenAI · Efficient

OpenAI o4-mini high

Added Dec 4, 2025

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Use in the app

The maximum/high version of the o4-mini model. The next generation of OpenAI models.

  • Reasoning
  • Tools
  • Structured output
Input
Text
Output
Text
Reasoning effort
low · medium · high
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$1.10
Output
$4.40
Cache read
$0.55

Reference prices as published by the model provider. API example

Specifications

Context window
200K
Max output
100K
Average output (7 days)
75 tokens
Longer than 7% of 468 models

Side by side

OpenAI o4-mini high compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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OpenAI o4-mini highOpenAIThis modelGemma 4 26B A4BGoogleRemove
ProviderOpenAIGoogle
Discovery tierEfficientOpen weights
AddedDec 4, 2025Apr 2, 2026
Limits
Context window200K262.14KBest
Max output100K131.07KBest
Average output75 tokens131 tokens
Pricing, USD per 1M tokens
Input$1.10$0.09Best
Output$4.40$0.30Best
Cache read$0.55$0.05Best
Performance
Throughput—65.0 tok/s
Time to first token—2.3 s
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index16.716.7
Coding Index—39.3
Math Index90.7—
Capabilities
ReasoningYesYes
ToolsYesYes
VisionNoYes
Structured outputYesYes
Video inputNoYes
Reasoning effortlow, medium, high—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • OpenAI o4-mini high
  • Gemma 4 26B A4B

Intelligence Index

  • OpenAI o4-mini high16.7
  • Gemma 4 26B A4B16.7

OpenAI o4-mini high tested at high reasoning effort

OpenAI o4-mini high: Better than 44% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 39.3

  • Math Index

    Composite of the math evaluations

    • OpenAI o4-mini high: 90.7
    • Gemma 4 26B A4B: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 1.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 0.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 315 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 565 Elo

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • OpenAI o4-mini high: 61.0%
    • Gemma 4 26B A4B: 65.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • OpenAI o4-mini high: 16.5%
    • Gemma 4 26B A4B: 19.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • OpenAI o4-mini high: 78.4%
    • Gemma 4 26B A4B: 79.2%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • OpenAI o4-mini high: 83.2%
    • Gemma 4 26B A4B: —

  • IFBench

    Following precise output instructions

    • OpenAI o4-mini high: 68.7%
    • Gemma 4 26B A4B: 72.4%Top

Coding

  • SciCode

    Scientific computing in Python

    • OpenAI o4-mini high: —
    • Gemma 4 26B A4B: 40.0%

  • LiveCodeBench

    Recent competitive programming problems

    • OpenAI o4-mini high: 85.9%
    • Gemma 4 26B A4B: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • OpenAI o4-mini high: 90.7%
    • Gemma 4 26B A4B: —

  • AIME

    Competition mathematics

    • OpenAI o4-mini high: 94.0%
    • Gemma 4 26B A4B: —

  • MATH-500

    Competition math problems

    • OpenAI o4-mini high: 98.9%
    • Gemma 4 26B A4B: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • OpenAI o4-mini high: 15.2%Top
    • Gemma 4 26B A4B: 13.6%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • OpenAI o4-mini high: 55.6%Top
    • Gemma 4 26B A4B: 43.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • OpenAI o4-mini high: 61.0%
    • Gemma 4 26B A4B: 65.7%Top

Scores from Artificial Analysis, updated Oct 26, 2026. OpenAI o4-mini high was tested at high reasoning effort; compare scores measured at the same effort.Artificial Analysis

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/o4-mini-high",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="openai/o4-mini-high",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

OpenAI

Other OpenAI models, starting with the same family.