Can I run o1-pro locally?

No. o1-pro is API-only with no open weights released, so local deployment is not possible.

What does o1-pro cost and what is its context window?

OpenAI prices it at $150.00 per million input tokens and $600.00 per million output tokens, with a 200,000-token context window.

How does o1-pro differ from standard o1?

Both use reinforcement-learning-based reasoning, but o1-pro allocates more compute per query, which OpenAI says produces more consistently correct answers on difficult problems — at a significantly higher price.

O1 pro

5509°

#llm #openai #vision #reasoning #research #coding

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OpenAI | 200,000 token context | $150.00/M input, $600.00/M output. 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.

Guides for this model

Open weights vs open source vs API-only: what you are actually allowed to do

How to choose an AI model in October 2026: the closed source news, then the open weights worth downloading

The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans

All guides

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

o1-pro is OpenAI's highest-compute reasoning model, built on the same reinforcement-learning foundation as o1 but given more time to think before it answers. That extra compute translates to more consistent performance on hard problems — the kind where a cheaper model occasionally slips. It suits researchers, engineers and analysts who need reliable answers on complex tasks and can absorb a serious API bill.

Known for

Extended internal reasoning before answering, reducing errors on hard problems.
200,000-token context window handles long documents and large codebases.
Vision capability alongside text reasoning.
Consistently better answers than standard o1 on the same difficult prompts.

Watch out for

At $150/M input and $600/M output, costs escalate fast — prototype elsewhere first.
Closed weights, API-only: no local deployment, no fine-tuning, no offline use.
Slower by design; the extra thinking time is not suitable for latency-sensitive applications.

Best for

Prompts to try

Audit a complex algorithm

Here is a recursive dynamic-programming solution for the weighted interval scheduling problem. Walk through the logic step by step, identify any edge cases it handles incorrectly, and rewrite the function to fix them. Explain each change you make and why it matters.

Stress-test a research argument

I am writing a paper arguing that mandatory code review reduces defect rates more than automated static analysis alone. List the three strongest counterarguments a hostile reviewer could raise, assess the evidence behind each, and suggest how I could address them in the paper.

Reason through a legal scenario

A SaaS company stores EU customer data on US servers under a standard contractual clause signed before the latest adequacy decision lapsed. Identify the compliance risks, rank them by severity, and outline the minimum steps the company should take to reduce exposure.

Compare with

gpt oss 120b Turbo
Open-weights model
131,072 token context · $0.15/M input, $0.60/M output
GPT-6 Luna
API-only model
1,050,000 token context · $0.10/M input, $0.50/M output
GPT-5.6 Sol
API-only model
1,050,000 token context · $4.00/M input, $20.00/M output
GPT-6.1 Sol
API-only model
1,050,000 token context · $2.00/M input, $10.00/M output

More text models from openai

Questions people ask

Can I run o1-pro locally?: No. o1-pro is API-only with no open weights released, so local deployment is not possible.
What does o1-pro cost and what is its context window?: OpenAI prices it at $150.00 per million input tokens and $600.00 per million output tokens, with a 200,000-token context window.
How does o1-pro differ from standard o1?: Both use reinforcement-learning-based reasoning, but o1-pro allocates more compute per query, which OpenAI says produces more consistently correct answers on difficult problems — at a significantly higher price.

Captain's verdict
The most reliable reasoning model OpenAI offers, but the price means you should be very sure you need it.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
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Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
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Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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