Docs/LLM

DeepSeek V4 Pro

1M contextCaché de promptsRazonamiento

POSThttps://api.foxwire.ai/v1/chat/completions

#Pricing Summary

Input Tokens

110,93 ₽ per 1M tokens. Corresponds to all context prompt tokens sent.

Output Tokens

221,85 ₽ per 1M tokens. Corresponds to content generated by the model.

Cache Read

1,02 ₽ per 1M tokens. Up to 90% savings when hitting the prompt cache.

#Solicitud de ejemplo

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": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

#Ejemplo de respuesta

Un objeto de chat completion estándar compatible con OpenAI. El campo model se normaliza al nombre que solicitaste.

json

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "model": "deepseek-v4-pro",
  "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.

#Caché de prompts

This model uses automatic (implicit) caching — repeated prompt prefixes are cached upstream automatically and billed at a lower rate, with no parameters needed (no cache_control). Keep long, stable context (system prompt, tools) at the start of your messages to get hits.

Automatic — nothing to do

Cache hits show up in the returned usage (cached tokens) and your cost drops automatically.

#Razonamiento

Para los modelos compatibles, añade reasoning_effort (low / medium / high). El razonamiento se devuelve en el campo reasoning_content de la respuesta; los tokens de razonamiento se facturan a la tarifa de salida.

json

{
  "model": "deepseek-v4-pro",
  "max_tokens": 2048,
  "reasoning_effort": "high",
  "messages": [{ "role": "user", "content": "your question" }]
}