GLM 5.2
https://api.sozdai.ai/v1/chat/completions#Pricing Summary
Input Tokens
178,5 ₽ per 1M tokens. Corresponds to all context prompt tokens sent.
Output Tokens
561 ₽ per 1M tokens. Corresponds to content generated by the model.
Cache Read
33,15 ₽ 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.
#Example request
Execute completions using standard OpenAI SDK formats by pointing base endpoints here.
curl https://api.sozdai.ai/v1/chat/completions \
-H "Authorization: Bearer $CORRY_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2",
"messages": [{"role": "user", "content": "Hello"}]
}'#Response example
A standard OpenAI-compatible chat completion object. The model field is normalized to the name you requested.
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"model": "glm-5.2",
"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
#Prompt caching
For cache-capable models, put the long static content (system, knowledge base) first and add a cache breakpoint. Cached portions can cost as little as 1/10. Cache hits are reported via the response usage field.
{
"model": "glm-5.2",
"messages": [
{
"role": "system",
"content": [
{ "type": "text", "text": "( long static context ... )", "cache_control": { "type": "ephemeral" } }
]
},
{ "role": "user", "content": "your question" }
]
}Prompt Caching Tip
#Thinking
For models that support it, add reasoning_effort (low / medium / high). The thinking is returned in the response's reasoning_content field; thinking tokens are billed at the output rate.
{
"model": "glm-5.2",
"max_tokens": 2048,
"reasoning_effort": "high",
"messages": [{ "role": "user", "content": "your question" }]
}
