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OpenAI · Balanced

OpenAI o1 Pro

Added Jan 25, 2025

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Use in the app

OpenAI's flagship series of reasoning models for solving hard problems. The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide consistently better answers. o1 pro comes with a massive 100,000 word output window.

  • Vision
  • PDF input
Input
Text, Images
Output
Text
Discovery tier
Balanced

Pricing

USD per 1M tokens
Input
$150.00
Output
$600.00
Cache read
$75.00

Reference prices as published by the model provider. API example

Specifications

Context window
200K
Max output
100K
Average output (7 days)
34 tokens
Longer than 2% of 468 models

Side by side

OpenAI o1 Pro compared

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

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OpenAI o1 ProOpenAIThis modelGemini 2.5 Flash Preview (09/2025)GoogleRemove
ProviderOpenAIGoogle
Discovery tierBalancedEfficient
AddedJan 25, 2025Sep 25, 2025
Limits
Context window200K1.05MBest
Max output100KBest65.54K
Average output34 tokens—
Pricing, USD per 1M tokens
Input$150.00$0.30Best
Output$600.00$2.50Best
Cache read$75.00$0.03Best
Performance
Request success (24h)—99.7%
Benchmark indexes
Intelligence Index12.412.4
Math Index—56.7
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
PDF inputYesYes
Audio inputNoYes
Reasoning effort—low, medium, high

Public evaluations

Benchmarks

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

  • OpenAI o1 Pro
  • Gemini 2.5 Flash Preview (09/2025)

Intelligence Index

  • OpenAI o1 Pro12.4
  • Gemini 2.5 Flash Preview (09/2025)12.4

OpenAI o1 Pro: Better than 26% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 56.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 60.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 8.7%
  • GPQA Diamond

    Graduate-level science questions

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 76.6%
  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 83.6%
  • IFBench

    Following precise output instructions

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 43.5%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 62.5%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 56.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 14.4%
  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 28.4%
  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • OpenAI o1 Pro: —
    • Gemini 2.5 Flash Preview (09/2025): 60.0%

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/o1-pro",
    "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/o1-pro",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

OpenAI

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