GPT 5.2 Pro
5857°
Run this model
Offers / Ads
OpenAI | 400,000 token context | $21.00/M input, $168.00/M output. GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like "think hard about this." Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.
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
Captain's notes
Notes from 2026-10-13
GPT-5.2 Pro is OpenAI's most capable model, built for high-stakes work where accuracy, careful reasoning, and long-document handling matter most. It improves on GPT-5 Pro in agentic coding, hallucination reduction, and sycophancy, and it responds to intent signals like 'think hard about this.' At $21 per million input tokens and $168 per million output, it is priced for professional and enterprise use, not casual experimentation.
Known for
- Agentic coding with meaningful gains over its predecessor
- 400,000-token context for large codebases or long documents
- Reduced hallucination and sycophancy compared to GPT-5 Pro
- Responds to user-specified reasoning intent at inference time
- Strong instruction following in health and high-stakes writing tasks
Watch out for
- Output costs $168 per million tokens — long agentic runs will produce large bills quickly.
- Closed weights, API-only: no local deployment, no fine-tuning on your own hardware.
- At this price tier, you should be certain you need the top of the range before committing.
Prompts to try
Audit a complex codebase
Here is a large Python service with multiple modules. Review the architecture for correctness, identify any logic errors or race conditions, suggest concrete refactors, and explain your reasoning step by step. Think hard about edge cases before you respond.
Reason through a hard decision
A hospital is deciding whether to adopt a new triage algorithm. The algorithm reduces average wait time by 12% but increases the rate of missed critical cases by 0.3%. Walk through the ethical, operational, and statistical considerations a decision-maker should weigh, and give a reasoned recommendation.
Analyse a long document
I am attaching a 200-page regulatory filing. Identify the five most material risk disclosures, explain what each one means in plain language, flag any internal inconsistencies you notice, and tell me which risks are standard boilerplate versus genuinely specific to this company.
Compare with
- gpt-oss-120bOpen-weights model131,072 token context · $0.15/M input, $0.60/M output
- gpt-oss-20bOpen-weights model131,072 token context · $0.02/M input, $0.09/M output
- GPT-6 LunaAPI-only model1,050,000 token context · $0.10/M input, $0.50/M output
- GPT-5.6 LunaAPI-only model1,050,000 token context · $0.20/M input, $1.20/M output
More text models from openai
Questions people ask
- Can I run GPT-5.2 Pro locally?
- No. OpenAI has not released weights for this model; it is available through the API only.
- What does GPT-5.2 Pro cost, and what is its context window?
- It costs $21.00 per million input tokens and $168.00 per million output tokens, with a 400,000-token context window.
- How is GPT-5.2 Pro different from GPT-5 Pro?
- OpenAI says it offers major improvements in agentic coding and long-context performance, with reductions in hallucination and sycophancy, plus support for test-time routing and user-specified reasoning intent.
Captain's verdictThe most capable model OpenAI has published, but the output price means you should have a genuine need before you reach for 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.
Ranking badge
Updated Oct 22, 2026
Made this model? Show this badge on your page.
Paste it into your Hugging Face card or Civitai description.
How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- 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.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- 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.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
