Repository-scale implementation
Trace an unfamiliar codebase, modify related files, run checks, and iterate through a well-scoped feature or repair.
Independent tool overview
GPT-5.3-Codex at a glance
GPT-5.3-Codex is OpenAI's agentic coding model for long-running software work, tool use, code review, terminal tasks, frontend development, and broader computer-based workflows.
Visit the official GPT-5.3-Codex site ↗
- Model type
- Agentic coding and computer-work model
- API model ID
- gpt-5.3-codex
- Context window
- 400,000 tokens
- Maximum output
- 128,000 tokens
- Knowledge cutoff
- August 31, 2025
- Reasoning effort
- Low, medium, high, and xhigh
- Modalities
- Text input/output and image input
Overview
What GPT-5.3-Codex is
GPT-5.3-Codex is a model optimized for agentic software engineering rather than a standalone code editor. It powers work inside Codex and can also be called directly through the OpenAI API in a custom agent harness.
The model is designed to inspect repositories, edit multiple files, use tools, run commands and tests, diagnose failures, review code, and continue through longer tasks. In Codex, users can steer it while it works instead of waiting for a single final response.
OpenAI's API catalog gives GPT-5.3-Codex a 400,000-token context window, up to 128,000 output tokens, text input and output, image input, function calling, structured outputs, streaming, and low, medium, high, or xhigh reasoning effort.
The release extends beyond code generation into deployment, monitoring, product documents, data analysis, research, and interface work. Those results depend heavily on the surrounding tools, permissions, environment, instructions, and validation loop.
GPT-5.3-Codex remains available in the API as model ID gpt-5.3-codex. Codex product access, model choices, speed modes, and usage limits depend on the user's ChatGPT plan, workspace policy, and client.
Use cases
Who GPT-5.3-Codex is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Complex debugging
Investigate failures across code, configuration, logs, tests, and runtime behavior when the agent has access to the relevant environment.
Code review and maintenance
Review diffs, identify likely regressions, improve tests, refactor carefully, and explain risk in the context of the actual repository.
Terminal and automation work
Use shell tools, scripts, APIs, and structured outputs inside a controlled agent harness.
Frontend and product work
Build interfaces, connect behavior, inspect rendered results, and iterate on implementation rather than returning only a code sample.
Long-running technical tasks
Handle research, implementation, testing, and documentation as one continuous workflow with compaction and clear acceptance criteria.
Capabilities
Core GPT-5.3-Codex features
Agentic software engineering
Optimized to plan, inspect, edit, test, debug, and continue across multiple steps instead of answering only with isolated snippets.
Interactive steering
Codex can accept feedback and follow-up direction while a task is active, preserving the working context.
Large context and output limits
Provides a 400,000-token context window and supports outputs up to 128,000 tokens for large repository and artifact workflows.
Adjustable reasoning
Supports low, medium, high, and xhigh effort; OpenAI recommends medium as a general balance and higher settings for the hardest tasks.
Tool and function calling
Can select structured tools inside an agent harness, allowing controlled access to files, commands, services, browsers, and other systems.
Vision input
Accepts images for tasks such as interpreting screenshots, interfaces, diagrams, and visual evidence; it does not natively generate images.
Structured outputs and streaming
Supports schema-constrained responses and streamed output for integrations that need predictable data or live progress.
First-class compaction
OpenAI's prompting guide describes compaction support for longer reasoning sessions and continuous conversations that would otherwise exceed context.
Broader professional workflows
Can work on specifications, research, data, documentation, presentations, and operational tasks when the harness provides the right tools.
Process
How the GPT-5.3-Codex workflow works
Step 1
Define the outcome
Provide the goal, repository or system in scope, acceptance criteria, constraints, and the evidence required before the work is considered complete.
Step 2
Choose the access boundary
Give the model only the files, tools, network access, credentials, and environments needed for the task.
Step 3
Load project guidance
Include repository instructions, architecture notes, test commands, coding conventions, and any protected or out-of-scope areas.
Step 4
Select reasoning effort
Use medium for typical interactive coding and reserve high or xhigh for difficult investigation, architecture, or long-horizon execution.
Step 5
Let the agent inspect first
Require it to ground decisions in current files, dependencies, runtime behavior, and documentation before changing the implementation.
Step 6
Keep consequential actions gated
Require approval for production writes, deployments, purchases, external messages, secrets, data deletion, or other difficult-to-reverse actions.
Step 7
Verify proportionally
Run focused tests, type checks, builds, security checks, and rendered or runtime verification appropriate to the change.
Step 8
Review the final diff
Inspect changed files, test evidence, assumptions, unresolved risks, and unrelated worktree changes before merging or deploying.
Cost
GPT-5.3-Codex pricing and free plan
GPT-5.3-Codex is available through Codex under ChatGPT plan allowances and directly through the OpenAI API. API text pricing is $1.75 per million input tokens, $0.175 per million cached input tokens, and $14 per million output tokens. Tool calls and surrounding infrastructure can add cost. The API free tier does not support this model.
Codex with ChatGPT
Included with plan access
Codex is included across ChatGPT plans, while available models, usage limits, credits, and speed options vary by plan and workspace.
- Available through supported Codex app, CLI, IDE, and web surfaces
- Check the model selector for GPT-5.3-Codex availability
- Usage dashboard and /status show current allowances
- Workspace administrators can restrict models and permissions
API input
$1.75 per 1M tokens
Standard uncached input-token price for direct API use.
- Cached input is $0.175 per 1M tokens
- Large repository context can materially affect cost
- Prompt caching can reduce repeated-prefix cost
- Rate limits depend on the API usage tier
API output
$14 per 1M tokens
Output pricing includes visible response tokens and applicable reasoning-token usage.
- Higher reasoning effort can increase token use and latency
- Tool-specific fees may apply when other OpenAI tools are called
- The model supports up to 128,000 output tokens
- Set project budgets and monitor usage in production
Pricing checked . Check current pricing at the source ↗
Assessment
GPT-5.3-Codex strengths and limitations
Where it stands out
- Strong fit for end-to-end software tasks rather than isolated code completion
- Large context window supports substantial code and evidence when retrieval is well managed
- Interactive steering makes long tasks easier to correct without restarting
- Multiple reasoning levels let teams trade latency and cost for deeper work
- Tool calling, structured outputs, streaming, and image input support custom agent integrations
- OpenAI publishes model specifications, prompting guidance, benchmark methodology, and a dedicated system card
- Direct API availability makes it usable outside the Codex product
What to consider
- The model can write plausible but incorrect code, misunderstand architecture, skip edge cases, or claim success without sufficient verification
- A 400,000-token window does not guarantee that every included detail will be used correctly; targeted retrieval and concise context still matter
- Long agent runs can consume significant output and reasoning tokens, tool time, and infrastructure budget
- The model has an August 2025 knowledge cutoff and needs current documentation or repository evidence for newer APIs and dependencies
- It cannot safely hold broad production permissions by default; commands, credentials, network access, and external writes need least-privilege controls
- Image input helps with screenshots but does not replace browser inspection, accessibility checks, device testing, or human design review
- Fine-tuning and predicted outputs are not supported for this model
- Benchmark scores are vendor-run evaluations and do not predict success on a specific codebase, language, framework, or internal workflow
- OpenAI classifies the model as High capability for cybersecurity and may route elevated-risk requests through additional safeguards or another model
- Generated changes still require ownership review, tests, security analysis, and a controlled deployment process
Compare
GPT-5.3-Codex alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Codex Max
Choose Codex Max when you want OpenAI's current long-horizon Codex model experience and are selecting through the Codex product rather than pinning this API model.
Explore Codex Max →Coding
GPT-5.3-Codex-Spark
Choose GPT-5.3-Codex-Spark for a faster, tighter interactive coding loop when low latency matters more than maximum long-horizon capability.
Explore GPT-5.3-Codex-Spark →Consumer
GPT-5.4 mini & nano
Choose GPT-5.4 mini or nano for cheaper high-volume coding, classification, or subagent work that does not need a frontier model.
Explore GPT-5.4 mini & nano →Coding
Claude Code
Choose Claude Code when comparing a mature terminal and IDE coding agent built around Anthropic's model ecosystem.
Explore Claude Code →Coding
Gemini CLI
Choose Gemini CLI for an open-source terminal agent and Google model workflow with a generous entry path.
Explore Gemini CLI →Questions
GPT-5.3-Codex FAQs
What is GPT-5.3-Codex?
GPT-5.3-Codex is OpenAI's agentic coding model for repository work, tool use, terminal tasks, debugging, review, frontend development, and broader professional workflows.
Is GPT-5.3-Codex the same as the Codex app?
No. GPT-5.3-Codex is a model. Codex is the agent product and harness that provides interfaces, tools, permissions, environments, task management, and model selection.
Can I use GPT-5.3-Codex through the API?
Yes. The current OpenAI model catalog lists gpt-5.3-codex for direct API use, including Responses and Chat Completions support.
How much does the GPT-5.3-Codex API cost?
OpenAI lists $1.75 per million input tokens, $0.175 per million cached input tokens, and $14 per million output tokens. Other tool calls and infrastructure are separate.
What context window does GPT-5.3-Codex have?
The model has a 400,000-token context window and supports up to 128,000 output tokens.
Which reasoning effort should I use?
OpenAI recommends medium as a general balance for interactive coding. High or xhigh can help with the hardest tasks, while low can reduce latency and cost for straightforward work.
Does GPT-5.3-Codex support images?
It accepts image input, which is useful for screenshots and visual context. It does not support native image, audio, or video output.
Can GPT-5.3-Codex deploy code autonomously?
It can use deployment tools if a harness grants access, but production changes should remain permissioned, reviewed, observable, and reversible. The model itself is not a deployment safety system.
Is GPT-5.3-Codex reliable enough to merge without review?
No. It can make broad, useful changes, but every result still needs tests and human review appropriate to the risk, especially for authentication, payments, security, data, infrastructure, and production behavior.
Bottom line
Our GPT-5.3-Codex verdict
GPT-5.3-Codex is a capable choice for teams that want a model to carry software work from investigation through implementation and verification, especially when they need direct API access and a large context window. Its value comes from the complete agent loop, not raw code generation. Give it strong repository guidance, narrow permissions, reliable tools, measurable acceptance criteria, and an independent review path.
Visit GPT-5.3-Codex website ↗Similar Coding Tools
View all Coding AI tools →
Qwen3-Coder-Next
Qwen3-Coder-Next - Alibaba's open-weight, small hybrid model for agentic coding
Composer 1.5
Composer 1.5 - Cursor's updated in-house agentic coding model with increased performance and speed
Codex App
Codex App - OpenAI's new Mac app interface for managing agents with its Codex agentic coding assistant
Oz
Oz - Warp's cloud platform for orchestrating hundreds of coding agents in parallel
AI training for the future of work.
Get access to all our AI courses, hundreds of real-world AI use cases, live expert-led workshops, an exclusive network of AI early adopters, and more.
AI Courses
Get unlimited access to all of our current & upcoming industry-specific AI courses for the duration of your subscription.
Daily Guides
To keep up with the rapid pace of AI, our team publishes AI implementation guides daily. Our library contains 300+ practical use cases to automate real-world work.
Workshops
Join weekly, live, interactive sessions with industry leaders who are at the forefront of AI for hands-on implementation guidance and exclusive insights.
Community
Network with an exclusive community of AI-first professionals who are working smarter with AI. Learn how early adopters are using AI in their work and businesses.