문서/LLM
GPT 6 Astra
1.1M context프롬프트 캐싱
POST
https://api.foxwire.ai/v1/chat/completions#Pricing Summary
Input Tokens
$5 per 1M tokens. Corresponds to all context prompt tokens sent.
Output Tokens
$25 per 1M tokens. Corresponds to content generated by the model.
Cache Read
$0.5 per 1M tokens. Up to 90% savings when hitting the prompt cache.
Cache Write
$0 per 1M tokens. Cost to index input context for caching.
#요청 예시
Execute completions using standard OpenAI SDK formats by pointing base endpoints here.
bash
curl https://api.foxwire.ai/v1/chat/completions \
-H "Authorization: Bearer $CORRY_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-6-astra",
"messages": [{"role": "user", "content": "Hello"}]
}'#응답 예시
표준 OpenAI 호환 채팅 컴플리션 객체입니다. model 필드는 요청한 이름으로 정규화됩니다.
json
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "gpt-6-astra",
"choices": [
{
"index": 0,
"message": { "role": "assistant", "content": "Hello! How can I help you today?" },
"finish_reason": "stop"
}
],
"usage": { "prompt_tokens": 12, "completion_tokens": 9, "total_tokens": 21, "cost": 0.000234, "currency": "USD" }
}Cost field
usage.cost is the amount charged for this call (in USD); in streaming it arrives on the final chunk that carries usage. OpenAI SDKs ignore this extra field.
#프롬프트 캐싱
캐시 지원 모델에서는 긴 정적 콘텐츠(시스템, 지식 베이스)를 먼저 배치하고 캐시 중단점을 추가하세요. 캐시된 부분은 비용이 최대 1/10까지 낮아질 수 있습니다. 캐시 적중은 응답의 usage 필드에 표시됩니다.
json
{
"model": "gpt-6-astra",
"messages": [
{
"role": "system",
"content": [
{ "type": "text", "text": "( long static context ... )", "cache_control": { "type": "ephemeral" } }
]
},
{ "role": "user", "content": "your question" }
]
}Prompt Caching Tip
Prompt caching matches request prefixes exactly. Make sure long contexts (e.g. system instructions, developer tools) appear at the start of your message list.
