Can I run GPT-5.3-Codex locally?

No. OpenAI has not released weights for this model; it is API-only and cannot be self-hosted.

What does GPT-5.3-Codex cost and what is its context window?

Pricing is $1.75 per million input tokens and $14.00 per million output tokens, with a 400,000-token context window.

How does GPT-5.3-Codex differ from earlier Codex models?

It adds the broader reasoning and professional knowledge of GPT-5.2 to the coding focus of GPT-5.2-Codex, and is approximately 25% faster and more token-efficient than its predecessor.

GPT 5.3 Codex

5118°

#llm #openai #vision #reasoning #coding #agents

Run this model

Offers / Ads

OpenRouter Terminal / API

Pay per token

Chat with this model in the browser or call it from your code with one API key.

OpenAI | 400,000 token context | $1.75/M input, $14.00/M output. GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results on SWE-Bench Pro and strong performance on Terminal-Bench 2.0 and OSWorld-Verified, reflecting improved multi-language coding, terminal proficiency, and real-world computer-use skills. The model is optimized for long-running, tool-using workflows and supports interactive steering…OpenAI | 400,000 token context | $1.75/M input, $14.00/M output. GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results on SWE-Bench Pro and strong performance on Terminal-Bench 2.0 and OSWorld-Verified, reflecting improved multi-language coding, terminal proficiency, and real-world computer-use skills. The model is optimized for long-running, tool-using workflows and supports interactive steering during execution, making it suitable for complex development tasks, debugging, deployment, and iterative product work. Beyond coding, GPT-5.3-Codex performs strongly on structured knowledge-work benchmarks such as GDPval, supporting tasks like document drafting, spreadsheet analysis, slide creation, and operational research across domains. It is trained with enhanced cybersecurity awareness, including vulnerability identification capabilities, and deployed with additional safeguards for high-risk use cases. Compared to prior Codex models, it is more token-efficient and approximately 25% faster, targeting professional end-to-end workflows that span reasoning, execution, and computer interaction.

Guides for this model

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

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

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

GPT-5.3-Codex is OpenAI's most capable agentic coding model, built for long-running, tool-using development workflows rather than one-shot completions. It combines frontier software engineering performance with broader reasoning and professional knowledge, and it is roughly 25% faster and more token-efficient than its predecessor. If your work spans writing, debugging, deploying and iterating on real software end-to-end, this is the model OpenAI built for that.

Known for

State-of-the-art results on SWE-Bench Pro for real-world software engineering tasks.
Supports interactive steering mid-execution, useful for long agentic coding runs.
Strong terminal and computer-use performance across multi-step workflows.
Handles structured knowledge work: documents, spreadsheets, slides, operational research.
Trained with vulnerability identification capabilities and extra cybersecurity safeguards.

Watch out for

Output pricing at $14.00/M tokens adds up fast in long agentic loops; model costs here.
Closed weights, API-only — you cannot run this locally or self-host it.
400,000-token context is generous but falls short of the 200k+ threshold for 'long context' class models that go higher.

Best for

Prompts to try

End-to-end feature build

You have access to a terminal and a Python codebase. Add a REST endpoint to the existing Flask app that accepts a JSON body with fields 'user_id' and 'amount', validates both, writes a row to the SQLite database, and returns a confirmation with the new row ID. Write the code, run the tests, fix any failures, and confirm the endpoint works.

Debug a failing deployment

My Docker container builds fine locally but crashes on startup in production with this error: 'ModuleNotFoundError: No module named dotenv'. The Dockerfile uses python:3.11-slim and installs dependencies from requirements.txt. Walk through the likely causes, check the relevant files, propose a fix, apply it, and verify the container starts cleanly.

Spreadsheet analysis task

I have a CSV with columns: date, region, product_sku, units_sold, unit_price. Calculate total revenue per region per month, identify the top three SKUs by revenue in each region, and flag any month where a region's revenue dropped more than 20% from the prior month. Output a clean summary table and a brief written interpretation.

Compare with

gpt-oss-120b
Open-weights model
131,072 token context · $0.15/M input, $0.60/M output
gpt-oss-20b
Open-weights model
131,072 token context · $0.02/M input, $0.09/M output
GPT-6 Luna
API-only model
1,050,000 token context · $0.10/M input, $0.50/M output
GPT-5.6 Luna
API-only model
1,050,000 token context · $0.20/M input, $1.20/M output

More text models from openai

Questions people ask

Can I run GPT-5.3-Codex locally?: No. OpenAI has not released weights for this model; it is API-only and cannot be self-hosted.
What does GPT-5.3-Codex cost and what is its context window?: Pricing is $1.75 per million input tokens and $14.00 per million output tokens, with a 400,000-token context window.
How does GPT-5.3-Codex differ from earlier Codex models?: It adds the broader reasoning and professional knowledge of GPT-5.2 to the coding focus of GPT-5.2-Codex, and is approximately 25% faster and more token-efficient than its predecessor.

Captain's verdict
The serious choice for multi-step agentic coding work, provided you can stomach $14/M output tokens in long runs.

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.

Change colors / personalize / get code

Not this creator?

Get your own badge

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.

Similar Models

AI Models for Image, Video & Text Generation

GPT 5.3 Codex is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.

More Text / LLM models llm openai vision reasoning coding agents

Browse by Model Family