Zhipu AI: GLM-4.5-Flash

Efficient GLM model for fast reasoning, coding, and agent workflows

Model lab: Zhipu AI
Model family: glm-flash

Modalitiestext → text

Context window131,072

Maximum output98,304

Input · Context ≤ 128000 tokens · Maximum output 96000 tokensUSD 0 / 1M tokens

Output · Context ≤ 128000 tokens · Maximum output 96000 tokensUSD 0 / 1M tokens

Try now

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-4.5-flash.

import os
import requests

base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "zhipuai/glm-4.5-flash"

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-4.5-Flash API Pricing

Type	Price information
Input · Context ≤ 128000 tokens · Maximum output 96000 tokens	USD 0 / 1M tokens
Cached input · Context ≤ 128000 tokens · Maximum output 96000 tokens	USD 0 / 1M tokens
Output · Context ≤ 128000 tokens · Maximum output 96000 tokens	USD 0 / 1M tokens

Performance

Performance trends

Throughput90.6 tok/sCurrent

Time to first token1.3 sCurrent

End-to-end latency7.1 sCurrent

Throughput

Current90.6 tok/s

Window average88.9 tok/s

Time to first token

Current1.3 s

Window average1.5 s

End-to-end latency

Current7.1 s

Window average7.5 s

Tail latency (P99)

Current17.5 s

Window average16.6 s

Prompt cache hit rate

Current19.2%

Window average22.5%

Error rate

Current0.0%

Window average0.1%

Availability

Uptime100%At least one provider responded

Availability100%Requests actually served

Error rate0%Share of failed requests

Served requests, hour by hour

24 hours agoNow

GLM-4.5-Flash Compare models

Model	Input price	Output price	Context window	Capabilities
GLM-4.5-FlashThis page	USD 0 / 1M tokens · Context ≤ 128000 tokens · Maximum output 96000 tokens	USD 0 / 1M tokens · Context ≤ 128000 tokens · Maximum output 96000 tokens	131,072	Text output · Reasoning · Tool calling
GLM-4.7-Flash	USD 0.048 / 1M tokens	USD 0.32 / 1M tokens	200,000	Text output · Reasoning · Tool calling
GLM-4.7-FlashX	USD 0.056 / 1M tokens	USD 0.32 / 1M tokens	200,000	Text output · Reasoning · Tool calling
GLM-5.3-Flash	USD 0.056 / 1M tokens	USD 0.176 / 1M tokens	1,000,000	Text output · Reasoning · Tool calling
GLM-4.6V-Flash	USD 0.01744 / 1M tokens · Base context tier (boundary 32000 tokens)	USD 0.17472 / 1M tokens · Base context tier (boundary 32000 tokens)	128,000	Text output · Reasoning · Tool calling

Model specifications

Input: text
Output: text
Context window: 131,072
Maximum output: 98,304
Release date: 2025-07-28
Attachments: No
Reasoning: Yes
Tool calling: Yes
Temperature setting: Yes
Open weights: No

Frequently asked questions

What is GLM-4.5-Flash for?

Efficient GLM model for fast reasoning, coding, and agent workflows

What inputs and outputs does GLM-4.5-Flash support?

Recorded inputs: text; outputs: text.

How is GLM-4.5-Flash priced?

The pricing table shows the current recorded Moduck price. For example: Input · Context ≤ 128000 tokens · Maximum output 96000 tokens USD 0 / 1M tokens; Output · Context ≤ 128000 tokens · Maximum output 96000 tokens USD 0 / 1M tokens. See the pricing table for all units, variants, and conditions.

What specifications are recorded for GLM-4.5-Flash?

Current model records list Context window 131,072 tokens; Maximum output 98,304 tokens.

What model parameter does the Quickstart use?

The example uses zhipuai/glm-4.5-flash. 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.