Zhipu AI: GLM-4.7
Mature GLM model for dependable coding, reasoning, and structured agent tasks
- Model lab
- Zhipu AI
- Model family
- glm
Model resources
Quickstart
Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to zhipuai/glm-4.7.
import os
import requests
base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "zhipuai/glm-4.7"
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model_id,
"messages": [{"role": "user", "content": "Hello!"}],
"stream": False,
},
timeout=60,
)
response.raise_for_status()
print(response.json())Use the familiar OpenAI-style chat request format to make model integration easier.
GLM-4.7 API Pricing
| Type | Price information |
|---|---|
| Input · Base context tier (boundary 32000 tokens) | USD 0.2191792 / 1M tokens |
| Output · Base context tier (boundary 32000 tokens) | USD 0.8767168 / 1M tokens |
| Cached input · Base context tier (boundary 32000 tokens) | USD 0.04383584 / 1M tokens |
Benchmarks
Reasoning
Performance
Measured on the Moduck gateway across live traffic, hour by hour over the last 24 hours. Time to first token is how long before anything appears; throughput is how fast the answer is written after that; end-to-end latency is the whole round trip. Figures are medians unless the card says otherwise.
Artificial Analysis measurement · Reasoning
| Measurement variant | Output speed | Time to first token | Time to first answer token | End-to-end response time |
|---|---|---|---|---|
| Reasoning | 38.99 tokens/s | 2.54 s | — | — |
Performance trends
Availability
Uptime is the share of time at least one provider serving this model responded. Availability is stricter: the share of requests actually served. A provider that answers but fails part of the traffic lowers availability without lowering uptime. Both cover the last three days.
GLM-4.7 Compare models
| Model | Input price | Output price | Context window | Capabilities |
|---|---|---|---|---|
| GLM-4.7This page | USD 0.2191792 / 1M tokens · Base context tier (boundary 32000 tokens) | USD 0.8767168 / 1M tokens · Base context tier (boundary 32000 tokens) | 204,800 | Text output · Reasoning · Tool calling |
| GLM-5.3 | USD 0.0632 / 1M tokens · Quantization fp4 | USD 1.12 / 1M tokens · Quantization fp4 | 1,000,000 | Text output · Reasoning · Tool calling |
| GLM-5.2 | USD 0.24 / 1M tokens | USD 0.84 / 1M tokens | 1,000,000 | Text output · Reasoning · Tool calling |
| GLM-5.1 | USD 0.36 / 1M tokens | USD 1.72 / 1M tokens | 200,000 | Text output · Reasoning · Tool calling |
| GLM-5-Turbo | USD 0.96 / 1M tokens | USD 3.2 / 1M tokens | 200,000 | Text output · Reasoning · Tool calling |
Model specifications
- Input
- text
- Output
- text
- Context window
- 204,800
- Maximum output
- 131,072
- Release date
- 2025-12-22
- Attachments
- No
- Reasoning
- Yes
- Tool calling
- Yes
- Temperature setting
- Yes
- Open weights
- Yes
Frequently asked questions
What is GLM-4.7 for?
Mature GLM model for dependable coding, reasoning, and structured agent tasks
What inputs and outputs does GLM-4.7 support?
Recorded inputs: text; outputs: text.
How is GLM-4.7 priced?
The pricing table shows the current recorded Moduck price. For example: Input · Base context tier (boundary 32000 tokens) USD 0.2191792 / 1M tokens; Output · Base context tier (boundary 32000 tokens) USD 0.8767168 / 1M tokens. See the pricing table for all units, variants, and conditions.
What specifications are recorded for GLM-4.7?
Current model records list Context window 204,800 tokens; Maximum output 131,072 tokens.
What model parameter does the Quickstart use?
The example uses zhipuai/glm-4.7. This value comes from the page identifier and is not confirmed as a callable Moduck public ID.
Can I call this model through the Moduck API now?
This page has no verified Moduck API call for this model. A Quickstart example or reserved ID does not establish access; check the models available to your account.