Repository-Scale Reasoning
Ingest and map dependencies across entire file systems using the 256k context window for architecture-aware refactoring.
GPT 5.2 Codex
OpenAI GPT 5.2 Codex is a high-reasoning, code-centric model built for complex repository-scale architecture. It features a 256k context window and agentic self-correction to handle autonomous debugging and large-scale legacy migrations.
API Usage Examples
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "gpt-5.2-codex",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'GPT 5.2 Codex pricing
Estimate a request with real work scenarios. GPTProto token pricing is 30% below official rates.
UsageQuantityRateCost
tokens
$1.225/1M$0.0018tokens
$9.8/1M$0.0078tokens
$0.1225/1M$0.0003tokens
$0.1225/1M$0.003Cost per request$0.0131
Requests
Top-up amount
Top-up $100 and you get:
1. Top-up credits with permanent validity. You will receive a total of $100.00.
2. Additional 30% model discount, saving $42.853 versus direct official OpenAI API calls.
Related Models
All Models| Model | Input → Output | ||||
|---|---|---|---|---|---|
 | 400K | $1.23 / $9.80 per 1M | $0.12 / $0.12 per 1M | Input: TextInput: ImageInput: Document Output: Text | |
| — | 1.31M | $0.15 / $0.50 per 1M | — / $0.03 per 1M | Input: TextInput: ImageInput: VideoInput: Document Output: Text | |
| — | 1.05M | $0.44 / $1.32 per 1M | — / $0.01 per 1M | Input: TextInput: Image Output: Text | |
| 1.31M | $1.26 / $3.96 per 1M | — / $0.23 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $0.45 / $2.25 per 1M | — / $0.04 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 500K | $1.20 / $3.60 per 1M | — / $0.30 per 1M | Input: TextInput: Image Output: Text | ||
| 1M | $1.80 / $5.40 per 1M | $2.25 / $0.23 per 1M | Input: TextInput: ImageInput: VideoInput: Document Output: Text | ||
| 1M | $4.50 / $22.50 per 1M | $5.63 / $0.45 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $0.45 / $2.25 per 1M | — / $0.04 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $0.18 / $1.50 per 1M | $0.02 / $0.02 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $2.70 / $13.50 per 1M | $0.27 / $0.27 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $0.16 / $0.96 per 1M | $0.20 / $0.02 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $1.60 / $9.60 per 1M | $2.00 / $0.16 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $4.00 / $24.00 per 1M | $5.00 / $0.40 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 500K | $1.20 / $3.60 per 1M | $0.30 / $0.30 per 1M | Input: TextInput: Image Output: Text | ||
| 1M | $1.80 / $9.00 per 1M | $2.25 / $0.18 per 1M | Input: TextInput: Document Output: Text | ||
| 1.05M | $0.48 / $0.96 per 1M | $0.10 / $0.10 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $1.26 / $3.96 per 1M | $0.23 / $0.23 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| — | $1.00 / $8.00 per 1M | $0.10 / $0.10 per 1M | Input: TextInput: ImageInput: Document Output: Text | ||
| 1M | $9.00 / $45.00 per 1M | $11.25 / $0.90 per 1M | Input: TextInput: Document Output: Text | ||
| 1M | $0.36 / $1.44 per 1M | $0.07 / $0.07 per 1M | Input: TextInput: Document Output: Text | ||
| — | 1.05M | $0.44 / $1.32 per 1M | — / $0.01 per 1M | Input: Text Output: Text | |
| — | 1.05M | $1.32 / $3.96 per 1M | — / $0.04 per 1M | Input: Text Output: Text | |
| 1M | $0.75 / $1.50 per 1M | $0.12 / $0.12 per 1M | Input: TextInput: Image Output: Text | ||
| 1.05M | $24.00 / $144.00 per 1M | — | Input: TextInput: ImageInput: Document Output: Text | ||
| 1.05M | $24.00 / $144.00 per 1M | — | Input: TextInput: ImageInput: Document Output: Text | ||
| 262K | $0.85 / $3.60 per 1M | $0.14 / $0.14 per 1M | Input: TextInput: Document Output: Text | ||
| 205K | $0.24 / $0.96 per 1M | $0.30 / $0.02 per 1M | Input: TextInput: Document Output: Text | ||
| 262K | $0.54 / $2.70 per 1M | $0.09 / $0.09 per 1M | Input: TextInput: Document Output: Text | ||
| — | $0.04 / $0.18 per 1M | $0.009 / $0.009 per 1M | Input: Text Output: Text | ||
| 262K | $0.10 / $0.97 per 1M | — | Input: TextInput: Image Output: Text | ||
| 262K | $0.10 / $0.97 per 1M | — | Input: TextInput: Image Output: Text | ||
| 262K | $0.02 / $0.18 per 1M | — | Input: TextInput: Image Output: Text |
OpenAI GPT 5.2 Codex Core Features
Explore the powerful features of OpenAI GPT 5.2 Codex. From 256k context to OpenAI vision, this GPT tool provides the Codex precision needed for professional software development.
Agentic Self-Correction
Features a plan-act-verify behavior that pre-simulates execution paths to reduce syntax errors by 40% over GPT-4o.
UI-to-Code Synthesis
Convert Figma exports or screenshots into responsive React or SwiftUI code with 90%+ visual accuracy using vision.
Zero-Shot Test Generation
Produce unit and integration tests with branch coverage exceeding 85% on the first pass for standard business logic.
OpenAI GPT 5.2 Codex Common Questions
Get answers about OpenAI GPT 5.2 Codex. Learn how this OpenAI model compares to others and why the OpenAI Codex architecture is the best choice for your GPT development workflow.
Is OpenAI GPT 5.2 Codex better for code than GPT-4o?
Yes. OpenAI optimized GPT 5.2 Codex specifically for software engineering logic. While GPT-4o is a versatile generalist, the 5.2 Codex branch uses agentic self-correction to pre-simulate code execution paths, reducing syntax errors by 40%. It also handles a 256k context window, which is double the capacity of many standard models, making it far superior for analyzing complex, multi-file repository dependencies and architectural patterns.
What is the context limit for OpenAI GPT 5.2 Codex?
The model supports a massive 256,000 token context window. This allows OpenAI GPT 5.2 Codex to ingest and understand large codebases or multiple documentation files simultaneously. By maintaining this broad perspective, the GPT 5.2 Codex engine can suggest refactoring strategies that respect local architectural patterns across your entire project, rather than just looking at isolated functions or snippets in a single file.
How does GPT 5.2 Codex handle legacy migrations?
OpenAI GPT 5.2 Codex is uniquely suited for legacy migration, such as moving COBOL to microservices. By utilizing the 256k context, the GPT model can analyze business rules hidden across thousands of lines of code. It creates a logic map of the legacy system and then generates modern, modular code (like Java or Python) that preserves the original functionality while optimizing for performance and modern cloud-native standards.
Can OpenAI GPT 5.2 Codex generate UI from images?
Yes. Leveraging its vision capabilities, OpenAI GPT 5.2 Codex can interpret Figma exports or screenshots. The GPT engine converts these high-fidelity designs into clean, responsive React, Tailwind, or SwiftUI code with over 90% visual accuracy. This makes GPT 5.2 Codex an essential tool for rapid prototyping, allowing frontend developers to move from a visual concept to a functional, state-managed codebase in minutes.
Does GPT 5.2 Codex support parallel tool use?
Absolutely. OpenAI built high-reliability parallel tool use into GPT 5.2 Codex. This allows the model to act as the brain for autonomous agents that need to call multiple APIs or execute several functions at once. In the 5.2 Codex environment, function calling is more deterministic, ensuring that complex tasks like automated debugging or CI/CD pipeline reviews are handled with the precision required for production systems.
What programming languages does this model support?
OpenAI GPT 5.2 Codex is proficient in over 80 programming languages. While it excels in popular languages like Python, JavaScript, Rust, and Go, its training also covers niche cloud SDKs and even legacy languages. The GPT 5.2 Codex architecture is designed to understand the underlying logic of syntax, making it just as effective at writing a lock-free queue in Rust as it is at generating comprehensive unit tests in PyTest or Jest.
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