GPT-5.3 Codex
GPT-5.3 Codex by OpenAI: $1.75 input and $14 output per 1M tokens, 400K context. Call it through the odnoga LLM gateway.
GPT-5.3 Codex is served by OpenAI and called through odnoga with the same OpenAI-compatible request shape as every other model in the catalog. It is a reasoning model, so it spends extra output tokens working through a problem before answering — budget for higher output cost on hard tasks. It accepts images alongside text. It supports tool and function calling. It can be forced to return structured JSON. Its 400K-token context window sets how much input you can send in one request. Cached input is billed at $0.175 per 1M tokens, so repeated prefixes cost less.
Specification and price
| Vendor | OpenAI |
|---|---|
| Model ID | gpt-5.3-codex |
| Context window | 400K tokens |
| Max output | 128K tokens |
| Capabilities | Reasoning, Vision, Tools, JSON mode, Streaming |
| Per 1M tokens | Vendor cost | Your price on Free+7% |
|---|---|---|
| Input | $1.75 | $1.88 |
| Output | $14 | $15.05 |
| Cached input | $0.175 | $0.188 |
Vendor cost is the list price per million tokens as recorded in the odnoga catalog; your price applies your plan margin with the same formula that bills every request — see pricing. Pricing
Call it through odnoga
const res = await fetch("https://api.odnoga.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.ODNOGA_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
model: "gpt-5.3-codex",
messages: [{ role: "user", content: "Hello" }],
}),
});
Same request shape for every vendor in the catalog — swap the model id and odnoga handles keys, routing, limits and cost accounting.
How to use GPT-5.3 Codex
Reviewed 2026-09-14
Best for
- Agentic coding: multi-file changes, refactors, test writing and long-running tasks in a Codex-style environment.
- Work that mixes research, tool use and execution over many steps rather than a single answer.
- Code review and bug hunting where the model needs the whole repository in context (400,000-token window).
- Pipelines where you steer the model while it works instead of waiting for one final output.
Not the right pick when
- General chat, writing or analysis — this model is tuned for agentic coding, not as a general assistant.
- Short, cheap code completions: a smaller general model is usually enough.
- Environments without sandboxed tool execution, where most of its advantage cannot be used.
Practical tips through odnoga
- 01Follow the Codex prompting guide: describe the goal, the constraints and how done is verified, then let the model run the loop.
- 02Use reasoning effort as the budget dial — the model supports low through xhigh.
- 03Long agent runs are where spend escapes: set a per-tenant budget and an alert before you let it run unattended.
- 04Record a real run with odnoga capture and freeze it as a test case, so the next model upgrade is compared on the work you actually do.
What a month costs
1,000 calls a month, 10,000 input tokens and 2,000 output tokens each, at your Free price (vendor cost +7%):
| Input (10M tokens) | $18.82 |
|---|---|
| Output (2M tokens) | $30.11 |
| Your cost per month on Free | $48.92 |
Vendor list cost $45.50 + odnoga margin $3.42 (+7%). Cached input or answer reuse lowers it; odnoga records both numbers per request.
GPT-5.3 Codex compared
| Model | Context | Input / 1M | Output / 1M | Capabilities |
|---|---|---|---|---|
| GPT-5.3 Codex | 400K | $1.75 | $14 | Reasoning, Vision, Tools, JSON mode, Streaming |
| ChatGPT (chat-latest) | 400K | $5 | $30 | Vision, Tools, JSON mode, Streaming |
| GPT Image 1 mini | — | $2 | $8 | Vision |
Questions
- What is GPT-5.3-Codex optimized for?
- OpenAI describes it as optimized for agentic coding tasks in Codex or similar environments, with low, medium, high and xhigh reasoning effort settings and a 400,000-token context window.
- Should I use it for everything in a developer tool?
- No. Use it for the agent loop and a cheaper general model for chat, summaries and metadata around it. odnoga lets both live behind the same endpoint with separate prompts and budgets.
- How do I keep an agent run from overspending?
- Cap reasoning effort, set a per-tenant budget in odnoga, and watch the per-request ledger — every step is recorded with its own cost.
All models · Pricing · Docs · Compare models in the evaluation lab
Sources: OpenAI — GPT-5.3-Codex model page, OpenAI — Codex prompting guide