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.
Related Gemini 3.5 Flash Models
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: '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);
}
}
}
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': '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)
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": "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
}'
Gemini 3.5 Flash Text to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Fast & Affordable Gemini 3.5 Flash Text-to-Text API (Google's Efficient Language Model)
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.
Key Features of Gemini 3.5 Flash Text-to-Text API
- Fast Text Generation: Produce useful responses quickly for chat, support, content, and workflow automation.
- Cost-Effective API Access: Use an affordable Gemini route for high-volume text-to-text applications on Flaq AI.
- Reliable Instruction Following: Convert natural-language instructions into focused answers, summaries, rewrites, and structured outputs.
- Conversation-Aware Output: Support follow-up questions and iterative refinement with flexible context.
- Developer-Friendly Controls: Guide response length and task direction through practical configuration on Flaq AI.
- Managed Google Integration: Add Gemini text capability through a stable API route with clear input and output behavior.
How to Use Gemini 3.5 Flash Text-to-Text API for Text Generation on Flaq AI
- Input: Natural language prompts, system instructions, conversation context, and structured task requirements.
- Output: Clear text responses delivered through a stable chat API integration on Flaq AI.
- Route Configuration: Designed for conversational text workflows with flexible context and follow-up support.
- Configuration: Supports practical output controls for response length and task direction.
- Capabilities: Summarization, rewriting, chat assistance, coding help, multilingual writing, and structured content generation through affordable Gemini API integration.
Best Use Cases for Gemini 3.5 Flash Text-to-Text API Integration
- High-Volume Chat Features: Power responsive assistants for apps, websites, and internal tools.
- Content Operations: Generate summaries, outlines, rewrites, and localized copy for publishing workflows.
- Support Automation: Draft answers, classify requests, and help users navigate product or policy information.
- Developer Productivity: Explain code, draft snippets, and summarize technical materials.
- Workflow Automation: Add fast language intelligence to dashboards, knowledge bases, and operational systems.
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 Text-to-Text vs Competitors: Comparative Analysis
-
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.