OpenAI / openai/gpt-5.3-codex
GPT-5.3-Codex - access through LLMTR
Targets repo-scale understanding, long edit chains, and demanding agentic coding workflows. One of the most ambitious Codex options for teams building coding products.
Technical specifications
| Canonical ID | openai/gpt-5.3-codex |
|---|---|
| Provider | OpenAI |
| Context window | 400,000 tokens |
| Operations | RESPONSES |
| Modalities | text, image |
Pricing
An 8% platform margin applies to credit top-ups; model usage prices are not separately marked up.
| Operation | Metric | Unit | Price |
|---|---|---|---|
| RESPONSES | INPUT_TEXT | PER_1M_TOKENS | $1.75 |
| RESPONSES | CACHE_READ | PER_1M_TOKENS | $0.175000 |
| RESPONSES | OUTPUT_TEXT | PER_1M_TOKENS | $14.00 |
Example usage
With existing OpenAI SDK flows, change only the base URL and model identifier.
curl https://llmtr.com/v1/chat/completions -H "Authorization: Bearer llmtr-your_key" -H "Content-Type: application/json" -d '{"model":"openai/gpt-5.3-codex","messages":[{"role":"user","content":"Hello"}]}'
Guides about this model
- An LLMTR model provider for Codex CLI and its Responses-only constraint - Codex CLI now speaks only the Responses protocol, so the model you pick on LLMTR must carry a Responses binding as well. The provider table, model choice and error triage all follow from that one constraint.
- HarnessRouter LLMTR provider: one key, three harnesses - Connecting the LLMTR provider in HarnessRouter adds no new name to any model picker; it makes models already listed reachable through Türkiye. Here is the intersection logic and the wire shape each harness uses.
- Trae Agent with LLMTR: Python configuration and trajectory records - Trae Agent reaches LLMTR through a dispatching class that splits models across two client implementations. This guide walks the Python configuration, the routing decision and the fields visible in the run record.
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