GLM 5.3
A 1M-context reasoning model built for complex coding and long-running agentic tasks.
Quantization
FP8
Open weights
Context Window
1.0M
Tokens
Intelligence
59.5
AA Index
Released
Aug 2026
25 Aug 2026
Frontier reasoning, open weights
GLM 5.3 is Z.ai's open-weight reasoning model for complex coding, long-horizon agentic work, tool use, cybersecurity analysis and repository-scale engineering.
It provides a 1M-token context window and configurable reasoning effort, so you can dial thinking budget up for hard planning problems and down for high-volume throughput — at a fraction of closed frontier pricing.
1M context · configurable reasoning effort
Best for repository-scale engineering
Complex Coding
Repository-scale engineering: multi-file edits, large refactors and iterative debugging across long sessions.
Agentic Workflows
Configurable reasoning effort for long-horizon planning, tool orchestration and recovery across many steps.
Cybersecurity Analysis
Reasoning over codebases, logs and configurations to surface vulnerabilities and explain exploit paths.
Long-Context Research
A 1M-token window keeps entire repositories, specs and document sets in a single request.
Also strong on research synthesis and enhanced structured generation.
Flexible Pricing Tiers
Choose the optimal balance of speed and cost for your workflow. Prices are per 1M tokens.
| Tier | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| Batch | $0.70 | $2.20 |
| Async | $1.05 | $3.30 |
| Realtime | $1.40 | $4.40 |
Context window natively supported up to 1,048,576 tokens. We can offer custom pricing for bulk discounts, large workloads, and dedicated deployments — reach out to hello@doubleword.ai.
Start Building in Minutes
GLM 5.3 is accessible via OpenAI-compatible endpoints. Default sampling: temperature=1.0, top_p=0.95.
from openai import OpenAI
client = OpenAI(
api_key="your-api-key-here",
base_url="https://api.doubleword.ai/v1"
)
# Long-horizon agentic coding task
response = client.chat.completions.create(
model="zai-org/GLM-5.3",
messages=[
{"role": "user", "content": "Audit this repository for unsafe deserialization and open a patch plan."}
],
temperature=1.0,
top_p=0.95,
)
print(response.choices[0].message.content)💡 Pro Tip
Route long-running agent steps through the async tier and bulk evals through batch — same model, same API, at half the realtime rate. See the docs for the batch job format.