OpenAI Chat API

Overview

OpenAI’s chat models on RouterBase, served through the standard OpenAI-compatible chat completions endpoint with streaming, tool calling, vision input, and per-user prompt caching. Covers the GPT-5 flagship family, the GPT-4 family, and the o-series reasoning models.


Provider	OpenAI
Families	`gpt-5`, `gpt-4`, `o-series`
Modality	Chat
Input	Text, image
Output	Text
Caching	Per-user prompt cache (automatic, isolated)

Models

openai/gpt-5.5
openai/gpt-5.4
openai/gpt-5.4-mini
openai/gpt-5
openai/gpt-5-nano
openai/gpt-4.1
openai/gpt-4.1-nano
openai/gpt-4o
openai/gpt-4o-mini
openai/o3
openai/o4-mini

Pricing is deliberately not reproduced here. Rates are set per model in the RouterBase catalogue and can be overridden per account, so any figure copied into this page is a snapshot that goes stale the next time a rate changes. Fetch the current rate for any model from the Models API:

GET /api/models/{model}/pricing

URL-encode the slash in the model id — e.g. openai%2Fgpt-5.5.

Endpoint

POST https://routerbase.com/v1/chat/completions

Drop-in compatible with the OpenAI Chat Completions API — point your existing OpenAI client at https://routerbase.com/v1 with your RouterBase key. See the full Chat Completions reference for every supported parameter.

Quickstart

curl https://routerbase.com/v1/chat/completions \
  -H "Authorization: Bearer $ROUTERBASE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-5.4",
    "messages": [
      { "role": "user", "content": "Explain prompt caching in one sentence." }
    ]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://routerbase.com/v1",
    api_key="$ROUTERBASE_API_KEY",
)

resp = client.chat.completions.create(
    model="openai/gpt-5.5",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(resp.choices[0].message.content)

Set "stream": true for token streaming (Server-Sent Events), exactly like the OpenAI API. Swap model for any id from the table above.

Prompt caching

RouterBase routes every customer through a shared upstream account but isolates each customer’s prompt cache automatically — your cached prefixes are never shared with, or served to, another customer. You don’t set anything: caching is on by default.

"usage": {
  "prompt_tokens": 1247,
  "completion_tokens": 89,
  "prompt_tokens_details": { "cached_tokens": 1200 }
}

Streaming responses carry the same breakdown on the final chunk’s usage. OpenAI’s automatic prefix cache needs a prompt of at least ~1024 tokens to take effect, so caching benefits long, stable system prompts and tool definitions.

Get Started

API Reference

Overview

Models

Endpoint

Quickstart

Prompt caching

​Overview

​Models

​Endpoint

​Quickstart

​Prompt caching

Overview

Models

Endpoint

Quickstart

Prompt caching