gpt-5.2-codex β API, Pricing & Context Window | Vivgrid
gpt-5.2-codex on Vivgrid: a Codex-tuned coding model on the Responses API with a 400K context window, function calling, and regional acceleration.
gpt-5.2-codex is a Codex-tuned member of the GPT-5 family, built for agentic coding on OpenAI's Responses API. It is a dependable choice for software-engineering agents that read, edit, and test code in a loop.
Vivgrid serves gpt-5.2-codex with geo-distributed acceleration across AMER and EMEA and a 400K-token context window, so it can hold substantial project context within a single session.
Specifications
| Provider | OpenAI |
| Model ID | gpt-5.2-codex |
| Best for | Coding |
| Context window | 400,000 tokens |
| Max output | 128,000 tokens |
| Modalities | Text, Image |
| Tool / function calling | Yes |
| Knowledge cutoff | 2025-08 |
| Acceleration | β‘ Geo-Distributed β AMER, EMEA |
Pricing
Pricing in USD per 1M tokens, matching the provider's rates.
| Input | Cached input | Output |
|---|---|---|
| $1.75 | $0.175 | $14.00 |
Quick start
Call gpt-5.2-codex through Vivgrid's unified, OpenAI-compatible endpoint. Get an API key from the Vivgrid Console.
curl https://api.vivgrid.com/v1/responses \
-H "Authorization: Bearer $VIVGRID_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-5.2-codex",
"input": "Refactor this function and explain the change.",
"stream": true
}'Ideal use cases
- Codex and Responses-API coding assistants
- Automated refactoring, test generation, and bug fixing
- Tool-calling agents that orchestrate shell and editor actions
- Cost-aware coding workloads that don't need the latest Codex release
Related models
- gpt-5.3-codex β the newer Codex generation
- gpt-5.1-codex β earlier Codex variant
- gpt-5.4 β general coding model on Chat Completions
gpt-5.3-codex
gpt-5.3-codex on Vivgrid: OpenAI's Codex-tuned coding model on the Responses API, with a 400K context window and geo-distributed acceleration.
gpt-5.1-codex-max
gpt-5.1-codex-max on Vivgrid: an extended-effort Codex coding model on the Responses API with a 400K context window and geo-distributed acceleration.