Z.ai/glm-5.2-text-to-text
glm-5.2-text-to-text
Free to try GLM 5.2 Text-to-Text API for reasoning, writing, coding help, summaries, and scalable LLM automation through stable Flaq AI workflows.
Text Chat
API Examples
Submit Example
const response = await fetch('https://api.flaq.ai/api/v1/chat/completions', {
method: 'POST',
headers: {
Authorization: 'Bearer YOUR_API_KEY',
Accept: 'text/event-stream',
'Content-Type': 'application/json'
},
body: JSON.stringify({
model: 'glm-5.2-text-to-text',
messages: [
{
role: 'user',
content: 'Write a four-line modern poem about a programmer drinking coffee.'
}
],
stream: true,
max_tokens: 500,
top_p: 0.5,
top_k: 1
})
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = '';
let assistantText = '';
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
const frames = buffer.split('\n\n');
buffer = frames.pop() || '';
for (const frame of frames) {
const lines = frame.split('\n').filter(Boolean);
let eventName = 'message';
const dataLines = [];
for (const line of lines) {
if (line.startsWith('event:')) {
eventName = line.slice(6).trim();
} else if (line.startsWith('data:')) {
dataLines.push(line.replace(/^data:\s*/, ''));
}
}
const raw = dataLines.join('\n').trim();
if (raw === '[DONE]') {
console.log('\nFinal text:', assistantText);
continue;
}
let payload;
try {
payload = JSON.parse(raw);
} catch {
continue;
}
if (eventName === 'error' || payload.error) {
const msg = payload.error?.message ?? payload.message ?? 'Chat request failed';
throw new Error(msg);
}
const delta = payload.choices?.[0]?.delta;
if (delta?.content) {
assistantText += delta.content;
console.log(assistantText);
}
}
}
Submit Example
import json
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/chat/completions',
headers={
'Authorization': 'Bearer YOUR_API_KEY',
'Accept': 'text/event-stream',
'Content-Type': 'application/json',
},
json={
'model': 'glm-5.2-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Write a four-line modern poem about a programmer drinking coffee.',
}
],
'stream': True,
'max_tokens': 500,
'top_p': 0.5,
'top_k': 1,
},
stream=True,
)
response.raise_for_status()
event_name = 'message'
assistant_text = ''
for raw_line in response.iter_lines(decode_unicode=True):
if not raw_line:
event_name = 'message'
continue
if raw_line.startswith('event:'):
event_name = raw_line.replace('event:', '', 1).strip()
continue
if raw_line.startswith('data:'):
raw_data = raw_line.replace('data:', '', 1).strip()
if raw_data == '[DONE]':
print('\nFinal text:', assistant_text)
continue
payload = json.loads(raw_data)
if event_name == 'error' or payload.get('error'):
error = payload.get('error') or payload
raise RuntimeError(error.get('message', 'Chat request failed'))
choices = payload.get('choices') or []
if choices:
delta = choices[0].get('delta') or {}
content = delta.get('content')
if content:
assistant_text += content
print(content, end='', flush=True)
Submit Example
curl -N -X POST "https://api.flaq.ai/api/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Accept: text/event-stream" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.2-text-to-text",
"messages": [
{
"role": "user",
"content": "Write a four-line modern poem about a programmer drinking coffee."
}
],
"stream": true,
"max_tokens": 500,
"top_p": 0.5,
"top_k": 1
}'
GLM 5.2 Text to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Stable & Affordable GLM 5.2 Text-to-Text API (Z.ai Agentic Reasoning Model)
GLM 5.2 Text-to-Text API on Flaq AI provides Z.ai language model access for reasoning, coding help, long-form writing, summarization, and agent-style automation. This affordable GLM API integration helps developers build stable LLM features through a managed route while avoiding provider-specific infrastructure work. It is designed for products that need practical instruction following, engineering assistance, and scalable text intelligence.
Key Features of GLM 5.2 Text-to-Text API
- Advanced Reasoning Workflows: Generate thoughtful answers for analysis, planning, coding support, and complex text tasks through GLM 5.2 API integration.
- Agentic Engineering Support: Build coding assistants, repo analysis tools, and task automation flows that benefit from structured reasoning and implementation guidance.
- Stable Text Generation: Produce reliable responses for chat, summarization, business writing, classification, and content transformation use cases.
- Developer-Friendly API Access: Connect GLM 5.2 to backend services, AI agents, and user-facing applications through Flaq AI's managed API route.
- Flexible Conversation Context: Support multi-turn workflows where the model needs to remember recent instructions, decisions, and user preferences.
- Cost-Effective LLM Integration: Use GLM 5.2 for affordable production language features without maintaining model hosting or custom serving infrastructure.
How to Use GLM 5.2 Text-to-Text API for Text Generation on Flaq AI
- Input: Natural language messages, system-style instructions, conversation context, and optional generation controls.
- Output: Text responses for chat, coding help, summaries, explanations, structured answers, and automation pipelines.
- Context: Supports conversational workflows for assistants, agents, support tools, and internal productivity systems.
- Capabilities: Reasoning, code assistance, long-form writing, summarization, classification, knowledge synthesis, and workflow automation through GLM 5.2 API integration.
Best Use Cases for GLM 5.2 Text-to-Text API Integration
- Coding & Engineering Assistants: Generate implementation plans, explain code, draft patches, summarize technical issues, and support developer workflows.
- AI Agent Backends: Power task decomposition, planning, decision support, and multi-step automation for agentic applications.
- Business Knowledge Work: Summarize documents, produce reports, draft emails, classify tickets, and transform unstructured notes into useful outputs.
- Research & Analysis Tools: Compare information, synthesize findings, reason through tradeoffs, and create structured analysis from user prompts.
- Customer Support Automation: Generate helpful replies, triage requests, and turn support context into clear next actions.
Note Please ensure prompts comply with Z.ai and Flaq AI safety requirements. If an error occurs, refine the request, remove restricted content, and try again.
GLM 5.2 Text-to-Text vs Competitors: Comparative Analysis
- GLM 5.2 vs. GPT Text-to-Text
GPT models are broad general-purpose LLMs. GLM 5.2 Text-to-Text API provides a Z.ai alternative focused on reasoning, coding help, and agentic engineering workflows through Flaq AI. - GLM 5.2 vs. Claude Text-to-Text
Claude is strong for careful writing and analysis. GLM 5.2 emphasizes practical engineering tasks, structured reasoning, and scalable automation. - GLM 5.2 vs. Kimi 2.7
Kimi 2.7 is a strong Moonshot AI option for coding and language workflows. GLM 5.2 gives teams another advanced LLM route with Z.ai model behavior and agent-oriented positioning. - GLM 5.2 vs. Qwen Max
Qwen Max provides Alibaba's advanced language capabilities. GLM 5.2 offers a separate model family for teams comparing reasoning style, coding behavior, and provider diversity. - GLM 5.2 vs. Gemini Flash
Gemini Flash prioritizes fast Google model access. GLM 5.2 is positioned for deeper reasoning, coding support, and agentic text workflows on Flaq AI.