Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Free to try Gemini 3.5 Flash API for fast LLM writing, coding help, reasoning, and summaries. Stable access for scalable production AI applications. Built for free testing and stable API workflows.
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: 'gemini-3.5-flash-text-to-text',
messages: [
{
role: 'user',
content: 'Write a concise summary of how transformer models work.'
}
],
stream: true,
max_tokens: 2048
})
});
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);
}
}
}
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': 'gemini-3.5-flash-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Write a concise summary of how transformer models work.',
}
],
'stream': True,
'max_tokens': 2048,
},
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)
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": "gemini-3.5-flash-text-to-text",
"messages": [
{
"role": "user",
"content": "Write a concise summary of how transformer models work."
}
],
"stream": true,
"max_tokens": 2048
}'
| Parameters | Price | Original Price | Discount |
|---|
Gemini 3.5 Flash Text-to-Text API on Flaq AI provides Google model access for fast, affordable, and production-ready language workflows. This efficient Gemini API integration helps developers build chat, summarization, writing, coding, and automation features with flexible conversation context and practical output controls. It is designed for teams that need responsive text generation at scale without managing Google model infrastructure directly.
Note Please ensure prompts and application workflows comply with Google's safety guidelines. If an error occurs, review the input for restricted content, simplify the request, and try again.
Gemini 3.5 Flash vs. Gemini Pro Models
Gemini Pro models are often positioned for more demanding reasoning and premium quality. Gemini 3.5 Flash prioritizes speed, affordability, and efficient throughput for everyday text workflows.
Gemini 3.5 Flash vs. GPT Models
GPT models offer OpenAI-native behavior and broad ecosystem familiarity. Gemini 3.5 Flash gives teams a fast Google model route for managed text generation on Flaq AI.
Gemini 3.5 Flash vs. Claude Sonnet
Claude Sonnet is strong for careful writing and balanced reasoning. Gemini 3.5 Flash is attractive for fast, cost-effective text generation and Google model integration.
Gemini 3.5 Flash vs. Qwen Plus
Qwen Plus provides affordable Alibaba language workflows. Gemini 3.5 Flash provides a Google alternative for responsive text-to-text applications.
Gemini 3.5 Flash vs. Llama
Llama models give teams open-model control. Gemini 3.5 Flash removes hosting complexity and provides managed API access for production text features.
Set up Flaq AI Claude models and explore Claude Code skills

Set up Flaq AI GPT models and explore Codex skills
Use Flaq AI LLM models in Hermes Agent
Use GLM 5.2, Kimi K3, and DeepSeek v4 in ZCode
Run DeepSeek Harness with Flaq AI DeepSeek models
Connect AI agents to Flaq image and video generation tools