Specs & substance
- Developed by
- Z.AI
- Model family
- GLM
- Use case
- Multimodal agentic
- Modality
- Text + Vision
- Context window
- 1M tokens
- Architecture
- MoE + hybrid attention
- Version
- 5.3 Flash
- License
- MIT
- Pricing
- $0.15 in · $0.50 out · $0.03 cache read
- Released
- Aug 2026
- Endpoint
- parasail-glm-53-flash
GLM-5.3 Flash is the first natively multimodal model in Z.AI’s GLM-5 series, built for coding and agentic workloads that mix text and images — frontend and game development, 3D simulation, browser and computer use — where the result has to be looked at, not just compiled. It outperforms GLM-5.2 across Z.AI’s coding and agentic benchmark suite while approaching Claude Opus 4.8, and scores 57 on the Artificial Analysis Intelligence Index v4.1.1 at a fraction of the cost that level of intelligence used to carry.
The model has 320B total parameters with 18B active across 45 layers — roughly half the activated parameters and half the layers of the GLM-4.5 series at a similar total size. It is the first GLM to combine linear and sparse attention in one hybrid stack: linear attention models local dependencies while a lightweight sparse indexer retrieves global context, with IndexPool compressing four indexer key vectors into one to hold latency and memory down at a 1M-token context. Manifold-Constrained Hyper-Connections (mHC) improve scaling efficiency, and pretraining runs on Z.AI’s 30T-token multimodal corpus.
Parasail serves GLM-5.3 Flash 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 Flash model-card results alongside selected comparison models.
All scores are self-reported by Z.AI in the GLM-5.3-Flash 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-Flash",
messages=[
{"role": "user", "content": "Implement Hello World in Python"}
],
stream=True,
stream_options={"include_usage": 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)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-Flash",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What is broken in this UI screenshot?"},
{"type": "image_url", "image_url": {"url": "https://example.com/screenshot.png"}}
]
}
],
max_tokens=1000
)
print(response.choices[0].message.content)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-Flash",
"messages": [{"role": "user", "content": "Implement Hello World in Python"}],
"stream": true,
"max_tokens": 1000
}'Explore the library
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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.
DeepSeek V4 Flash
Ultra-fast, ultra-cheap reasoning model for high-throughput workloads.
GLM-5.1
Strong reasoning and coding model with a 1M context window at competitive pricing.
Run GLM-5.3 Flash on Parasail.
Call it on an OpenAI-compatible endpoint with per-token pricing, or ask us about dedicated capacity.