gpt-5.3-codex β API, Pricing & Context Window | Vivgrid
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-5.3-codex is OpenAI's Codex-optimized coding model, tuned for agentic software engineering: editing files, running tools, and iterating in a loop. It is served on the Responses API (/responses), the surface that Codex and similar coding CLIs expect.
With a 400K-token context window and geo-distributed acceleration across AMER and EMEA, it delivers low-latency, long-horizon coding sessions on Vivgrid through a single API key.
Specifications
| Provider | OpenAI |
| Model ID | gpt-5.3-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-5.3-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-5.3-codex",
"input": "Refactor this function and explain the change.",
"stream": true
}'Ideal use cases
- Codex CLI and other Responses-API coding agents
- Iterative, tool-driven software engineering tasks
- Repository-scale edits within a 400K-token budget
- Pipelines that benefit from streamed, incremental output
Related models
- gpt-5.2-codex β the prior Codex generation
- gpt-5.1-codex-max β extended-effort Codex variant
- gpt-5.5 β flagship model on the Chat Completions API
gpt-5.4
Use OpenAI's gpt-5.4 on Vivgrid: a 1.05M-token coding model with broad three-region acceleration (AMER, EMEA, APAC) and a unified, OpenAI-compatible API.
gpt-5.2-codex
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.