Docs

# scores documents by relevance to the query; billed per document
curl https://gigarouter.ai/v1/rerank \
  -H "Authorization: Bearer $GR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"cross-encoder/ms-marco-MiniLM-L6-v2",
       "query":"how do I reset my password",
       "documents":["Password reset steps...","Billing FAQ..."]}'

# plain requests - no SDK needed
import requests
r = requests.post("https://gigarouter.ai/v1/rerank",
  headers={"Authorization": f"Bearer {KEY}"},
  json={"model": "cross-encoder/ms-marco-MiniLM-L6-v2",
        "query": query,
        "documents": docs})
for hit in r.json()["results"]:
    print(hit["index"], hit["relevance_score"])

# the OpenAI SDK works - just change base_url
from openai import OpenAI
client = OpenAI(base_url="https://gigarouter.ai/v1", api_key=KEY)
v = client.embeddings.create(
  model="Qwen/Qwen3-Embedding-0.6B", input=["hello world"])
print(v.data[0].embedding[:4])

• Pricing — rerank per 1k documents, embeddings per 1M tokens. Send X-Tier: batch for the discounted flexible tier.
• Cold start — a rarely-used model may be asleep; the first call returns 503 + Retry-After while it warms (about 90s). Retry, or send Prefer: wait=30 to hold the request. Popular models stay warm.
• Balance — prepaid. Check it at GET /account?key=.... Out of credit? Email us to top up (self-serve billing is coming).