GLM-5.3 Uncensored API
unst33r-proUNCENSOREDThe uncensored GLM 5.3 checkpoint, hosted as unst33r-pro on one OpenAI-compatible endpoint — no GPUs, no weights to download — for agent workflows and authorized security testing.
PRICING & SPECIFICATIONS
- Checkpoint family
- GLM-5.3
- Variant
- Uncensored
- API model id
- unst33r-pro
- Input price
- $2.50 / 1M tokens
- Output price
- $4.50 / 1M tokens
- Streaming
- Supported
- Tool calling
- Supported
This endpoint routes the full GLM-5.3 Uncensored checkpoint. It is distinct from GLM-5.3-Flash and is not a GLM-4.6 API alias.
Call GLM-5.3 with Python
Install the OpenAI Python SDK, set your UNST33R key, and keep the model ID exactly as shown.
from openai import OpenAI
client = OpenAI(
base_url="https://api.unst33r.si/v1",
api_key="<your api key>",
)
response = client.chat.completions.create(
model="unst33r-pro",
messages=[{"role": "user", "content": "Explain this code."}],
)
print(response.choices[0].message.content)Need the full setup? Follow the OpenAI Python base URL guide.
Call the API with curl
curl https://api.unst33r.si/v1/chat/completions \
-H "Authorization: Bearer $UNST33R_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "unst33r-pro",
"messages": [{"role": "user", "content": "Hello"}]
}'Tool calling status
Tool calling is enabled for unst33r-pro. Send OpenAI-style function definitions in the tools array, execute the selected function in your application, then return its result to the model.
response = client.chat.completions.create(
model="unst33r-pro",
messages=[{"role": "user", "content": "Weather in Ljubljana?"}],
tools=[{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a city",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
},
}],
)Benchmark status
UNST33R has not published a benchmark result for this exact GLM-5.3 Uncensored checkpoint. We do not transfer scores from a base model, another variant, or an earlier model family. Compare it on your own prompts and tool loops before choosing it for production work.
Self-hosted weights or a hosted uncensored API?
Abliterated and uncensored checkpoints of this family are also distributed as downloadable weights. Running them yourself means sourcing the weights, sizing a GPU box, serving an inference stack, and keeping it updated. A hosted API skips all of it:
- · No GPU rental or capacity planning — usage is billed per token.
- · No weights to download, store, or re-patch when a new revision ships.
- · OpenAI-compatible request shape — moving from a local server is mostly a base URL change.
- · Streaming and tool calling per the capability table above.
Self-hosting still wins when you need full weight custody or air-gapped inference. For everything else, unst33r-pro serves the same checkpoint behind an API key.