Gemini 3.5 Flash API for visual Q&A, OCR, image analysis, and multimodal reasoning. Stable access for production AI applications. Test the model output, compare visual quality, and scale successful ideas through stable image, video, or multimodal API workflows for teams.
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-image-to-text',
messages: [
{
role: 'user',
content: [
{ type: 'text', text: 'Describe the image and extract any visible text.' },
{
type: 'image_url',
image_url: {
url: 'https://example.com/sample-image.jpg'
}
}
]
}
],
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-image-to-text',
'messages': [
{
'role': 'user',
'content': [
{'type': 'text', 'text': 'Describe the image and extract any visible text.'},
{
'type': 'image_url',
'image_url': {
'url': 'https://example.com/sample-image.jpg'
}
},
],
}
],
'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-image-to-text",
"messages": [
{
"role": "user",
"content": [
{ "type": "text", "text": "Describe the image and extract any visible text." },
{
"type": "image_url",
"image_url": {
"url": "https://example.com/sample-image.jpg"
}
}
]
}
],
"stream": true,
"max_tokens": 2048
}'
Gemini 3.5 Flash Image to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Fast & Affordable Gemini 3.5 Flash Image-to-Text API (Google's Efficient Vision Understanding Model)
Gemini 3.5 Flash Image-to-Text API on Flaq AI provides Google model access for fast visual understanding, image analysis, and multimodal reasoning workflows. This efficient Gemini API integration helps developers turn uploaded images into descriptions, extracted details, answers, and structured summaries through a stable managed route. It is designed for teams that need responsive image-to-text capability without building provider-specific infrastructure.
Key Features of Gemini 3.5 Flash Image-to-Text API
- Fast Vision Understanding: Analyze images, screenshots, objects, layouts, and visual details with responsive Gemini model behavior.
- Image-Grounded Answers: Ask natural-language questions about uploaded images and receive focused text responses.
- Cost-Effective API Access: Build high-volume visual analysis workflows through an affordable managed Gemini route.
- Conversation-Aware Output: Support follow-up questions and iterative image review with flexible context.
- Developer-Friendly Controls: Guide response length, detail level, and task direction through practical configuration on Flaq AI.
- Production-Ready Integration: Add image-to-text capability to apps, tools, and internal workflows without managing model infrastructure.
How to Use Gemini 3.5 Flash Image-to-Text API for Visual Analysis on Flaq AI
- Input: Uploaded image content plus natural-language instructions describing the analysis, extraction, or reasoning task.
- Output: Text descriptions, extracted details, visual reasoning, or structured summaries from uploaded images.
- Route Configuration: Designed for focused image understanding workflows with route-specific image input support.
- Configuration: Supports practical output controls for response length, detail level, and task direction.
- Capabilities: Image understanding, OCR-style extraction, visual QA, screenshot review, product inspection, and multimodal reasoning through affordable Gemini API integration.
Best Use Cases for Gemini 3.5 Flash Image-to-Text API Integration
- Screenshot & UI Review: Extract key details from interface captures, dashboards, and product screenshots.
- Product & Catalog Analysis: Generate product descriptions, identify attributes, and summarize visual differences.
- Document Image Understanding: Read visual layouts, forms, receipts, and image-based reference material.
- Creative Asset QA: Review ads, social visuals, mockups, and design exports for content details.
- Accessibility Workflows: Produce image descriptions and visual summaries that make media easier to understand and reuse.
Note Please ensure prompts, uploaded images, 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 Image-to-Text vs Competitors: Comparative Analysis
-
Gemini 3.5 Flash vs. GPT Image-to-Text
GPT image-to-text routes offer OpenAI-native multimodal behavior. Gemini 3.5 Flash provides a fast Google model route for cost-effective visual understanding on Flaq AI. -
Gemini 3.5 Flash vs. Claude File Analysis
Claude file analysis is strong for careful document and attachment reasoning. Gemini 3.5 Flash Image-to-Text focuses on responsive image understanding and visual QA. -
Gemini 3.5 Flash vs. Grok Image-to-Text
Grok Image-to-Text offers xAI model behavior for visual analysis. Gemini 3.5 Flash provides a Google alternative with efficient managed API access. -
Gemini 3.5 Flash vs. Qwen Vision Workflows
Qwen vision workflows are attractive for Alibaba model users. Gemini 3.5 Flash is a strong fit for teams that want fast Google image-to-text integration. -
Gemini 3.5 Flash vs. Llama Vision Models
Llama vision models offer open-model deployment flexibility. Gemini 3.5 Flash removes hosting work and gives developers a stable managed route.