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OpenAI · Efficient

OpenAI o3-mini (High)

Added Jan 1, 2024

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Use in the app

OpenAI's o3-mini reasoning model with reasoning effort set to high.

  • 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

Side by side

OpenAI o3-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 o3-mini (High)OpenAIThis modelOpenAI o4-mini highOpenAIRemove
ProviderOpenAIOpenAI
Discovery tierEfficientEfficient
AddedJan 1, 2024Dec 4, 2025
Limits
Context window200K200K
Max output100K100K
Average output—75 tokens
Pricing, USD per 1M tokens
Input$1.10$1.10
Output$4.40$4.40
Cache read$0.55$0.55
Benchmark indexes
Intelligence Index12.516.7Best
Math Index—90.7
Capabilities
ReasoningYesYes
ToolsYesYes
Structured outputYesYes
Reasoning effortlow, medium, highlow, medium, high

Public evaluations

Benchmarks

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

  • OpenAI o3-mini (High)
  • OpenAI o4-mini high

Intelligence Index

  • OpenAI o3-mini (High)12.5
  • OpenAI o4-mini high16.7

OpenAI o3-mini (High): Better than 27% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • OpenAI o3-mini (High): —
    • OpenAI o4-mini high: 90.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • OpenAI o3-mini (High): —
    • OpenAI o4-mini high: 61.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • OpenAI o3-mini (High): 7.9%
    • OpenAI o4-mini high: 16.5%Top

  • GPQA Diamond

    Graduate-level science questions

    • OpenAI o3-mini (High): 74.8%
    • OpenAI o4-mini high: 78.4%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • OpenAI o3-mini (High): 79.1%
    • OpenAI o4-mini high: 83.2%Top

  • IFBench

    Following precise output instructions

    • OpenAI o3-mini (High): —
    • OpenAI o4-mini high: 68.7%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • OpenAI o3-mini (High): 71.7%
    • OpenAI o4-mini high: 85.9%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • OpenAI o3-mini (High): —
    • OpenAI o4-mini high: 90.7%

  • AIME

    Competition mathematics

    • OpenAI o3-mini (High): 77.0%
    • OpenAI o4-mini high: 94.0%Top

  • MATH-500

    Competition math problems

    • OpenAI o3-mini (High): 97.3%
    • OpenAI o4-mini high: 98.9%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • OpenAI o3-mini (High): 6.8%
    • OpenAI o4-mini high: 15.2%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • OpenAI o3-mini (High): 28.7%
    • OpenAI o4-mini high: 55.6%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • OpenAI o3-mini (High): —
    • OpenAI o4-mini high: 61.0%

Scores from Artificial Analysis, updated Oct 26, 2026. 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/o3-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/o3-mini-high",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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