Specs & substance
- Developed by
- Z.AI
- Model family
- GLM
- Use case
- Agentic coding
- Modality
- Text
- Context window
- 1M tokens
- Architecture
- MoE + DSA
- Version
- 5.3
- License
- GLM-5.3 License
- Pricing
- $1.40 in · $4.40 out · $0.26 cache read
- Released
- Aug 2026
- Endpoint
- parasail-glm-53
GLM-5.3 reuses the GLM-5.2 base model and pulls every improvement out of post-training instead. It is the most capable open-weights model for coding Z.AI has published, with a 50% gain over GLM-5.2 on its in-house Z.ai Code Bench and open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents’ Last Exam — and it holds that lead further up long-horizon and agentic tasks.
The surprise of the release is what emerged at scale: cyber capability developed faster than Z.AI expected. GLM-5.3 is state of the art on CyberGym for vulnerability discovery, and its advantage widens further up the exploitation chain — it more than doubles GLM-5.2 on the exploitation benchmarks ExploitGym and ExploitBench. The MoE + DSA architecture carries a 1M-token context window, and the thinking budget is tunable through the `reasoning_effort` parameter at `low`, `high`, and the default `max` levels.
Parasail serves GLM-5.3 on a global fleet of current-gen GPUs behind a single OpenAI-compatible endpoint — with per-token pricing, no minimums, and dedicated capacity options when you need guaranteed throughput.
Measured on the work that matters
GLM-5.3 model-card results alongside selected comparison models.
All scores are self-reported by Z.AI in the GLM-5.3 model card on Hugging Face ; the comparison columns are Z.AI’s own evaluations, published with the release. Where a benchmark is reported both with and without tool augmentation, we show the lower, no-tools number.
Drop-in via the OpenAI SDK
Point any OpenAI-compatible client at Parasail and change the model name. That's it.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.parasail.io/v1"
)
response = client.chat.completions.create(
model="zai-org/GLM-5.3",
messages=[
{"role": "user", "content": "Implement Hello World in Python"}
],
stream=True,
stream_options={"include_usage": True},
extra_body={"clear_thinking": True},
top_p=1,
max_tokens=1000,
temperature=1
)
for chunk in response:
if chunk.choices and chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="", flush=True)curl https://api.parasail.io/v1/chat/completions \
-H "Authorization: Bearer $PARASAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org/GLM-5.3",
"messages": [{"role": "user", "content": "Implement Hello World in Python"}],
"stream": true,
"clear_thinking": true,
"max_tokens": 1000
}'{
"id": "chatcmpl-143",
"object": "chat.completion",
"created": 1741224586,
"model": "zai-org/GLM-5.3",
"choices": [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "print(\"Hello, World!\")"
}
}
],
"usage": {
"prompt_tokens": 38,
"completion_tokens": 12,
"total_tokens": 50
}
}Explore the library
GLM-5.3 Flash
Z.AI’s first natively multimodal GLM-5 model — 320B total / 18B active parameters at a tenth of GLM-5.2’s price.
GLM-5.2
Z.AI’s next-gen model for agentic engineering with sustained execution on long-horizon tasks.
Kimi K3
Moonshot's 2.8T-parameter open-weight multimodal agentic model — frontier reasoning, native vision, and a 1M-token context window.
GLM-5.1
Strong reasoning and coding model with a 1M context window at competitive pricing.
Run GLM-5.3 on Parasail.
Call it on an OpenAI-compatible endpoint with per-token pricing, or ask us about dedicated capacity.