Try Gemini 3.6 Flash API for file analysis, document review, extraction, summaries, and multimodal reasoning through Flaq AI's stable Google API access.
Related Gemini 3.6 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-3.6-flash-file-analysis',
messages: [
{
role: 'user',
content: [
{ type: 'text', text: 'Summarize this file and extract the key risks.' },
{
type: 'file',
file: {
filename: 'demo.pdf',
file_data: 'https://example.com/demo.pdf'
}
}
]
}
],
stream: true,
max_tokens: 4096
})
});
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-3.6-flash-file-analysis',
'messages': [
{
'role': 'user',
'content': [
{'type': 'text', 'text': 'Summarize this file and extract the key risks.'},
{
'type': 'file',
'file': {
'filename': 'demo.pdf',
'file_data': 'https://example.com/demo.pdf'
}
},
],
}
],
'stream': True,
'max_tokens': 4096,
},
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-3.6-flash-file-analysis",
"messages": [
{
"role": "user",
"content": [
{ "type": "text", "text": "Summarize this file and extract the key risks." },
{
"type": "file",
"file": {
"filename": "demo.pdf",
"file_data": "https://example.com/demo.pdf"
}
}
]
}
],
"stream": true,
"max_tokens": 4096
}'
Gemini 3.6 Flash File Analysis Pricing
| Parameters | Price | Original Price | Discount |
|---|
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Fast and Efficient Gemini 3.6 Flash File Analysis API
Gemini 3.6 Flash File Analysis API brings Gemini's multimodal understanding into a Flaq AI route designed for file-centered questions. It helps applications combine written instructions with uploaded source material, and optionally a visual reference, to produce clear text responses for analysis, extraction, summarization, and review workflows. The integration supports a bounded multi-file request pattern, so teams can keep document-oriented tasks organized without overloading a general chat flow.
Key Features of Gemini 3.6 Flash File Analysis API
-
File-Centered Analysis: Ask questions about uploaded source material and receive text that summarizes, explains, organizes, or extracts the most relevant information.
-
Multimodal Context Support: Combine file content with written instructions and, where useful, a supporting image to give the model clearer analytical context.
-
Efficient Long-Context Workflows: Use the Gemini Flash model family for document-heavy tasks that benefit from context-aware reasoning and concise output.
-
Bounded Multi-File Requests: Keep related source material together in the supported Flaq AI file-input workflow rather than splitting an analysis task across many isolated prompts.
-
Structured Conversation Control: Add system instructions and follow-up user messages to define output format, audience, tone, and review criteria.
-
Text Output for Downstream Use: Return readable analysis that can feed internal dashboards, review queues, search experiences, and human approval steps.
How to Use Gemini 3.6 Flash File Analysis API on Flaq AI
-
Input: Supported file inputs with a natural-language instruction, plus an optional image when it helps explain the source material.
-
Output: Text summaries, findings, extracted details, comparisons, and analysis drafts based on the supplied request context.
-
Request Scope: Use the configured multi-file limit to keep each request focused on a coherent body of source material.
-
Capabilities: Document analysis, file-grounded question answering, summarization, information extraction, and review-assistant workflows.
Best Use Cases for Gemini 3.6 Flash File Analysis API Integration
-
Document Review: Summarize internal reports, proposals, specifications, and research packs before a human reviewer makes a decision.
-
Knowledge Base Preparation: Extract key facts, generate structured notes, and create first-pass answers from uploaded business material.
-
Operations and Compliance Support: Help teams identify relevant passages and prepare reviewable checklists without replacing required expert validation.
-
Research Synthesis: Compare supplied source files, organize findings by topic, and draft evidence-oriented summaries for analysts.
-
Workflow Automation: Route file-based requests through an API that produces concise textual output for ticketing, review, and internal tooling.
Note Flaq AI exposes a bounded file-analysis workflow, not an unlimited archive-processing service. Validate source-grounded answers against the original material and apply appropriate privacy, retention, and access controls to uploaded files.
Gemini 3.6 Flash File Analysis API vs Competitors: Comparative Analysis
-
Gemini 3.6 Flash File Analysis vs. Gemini 3.5 Flash File Analysis
Gemini 3.5 Flash remains suitable for routine file and text workflows. Gemini 3.6 Flash is the newer generation for teams that want efficient reasoning and stronger support for complex multimodal tasks. -
Gemini 3.6 Flash File Analysis vs. Gemini 3.7 Flash File Analysis
Gemini 3.7 Flash is the newer Flash option for more complex workflows. Gemini 3.6 Flash offers a balanced alternative when the application needs a focused, file-grounded analysis route with efficient configuration. -
Gemini 3.6 Flash File Analysis vs. GPT 5.6 Terra File Analysis
GPT 5.6 Terra file-analysis models are a broad option for document-centric applications. Gemini 3.6 Flash gives teams a Gemini-based route to evaluate for source synthesis, extraction, and review assistance. -
Gemini 3.6 Flash File Analysis vs. Claude Document Analysis
Claude models are often considered for long-form document reasoning. Gemini 3.6 Flash provides another multimodal option for workflows that need clear text output from a bounded set of uploaded files. -
Gemini 3.6 Flash File Analysis vs. Gemini 3.6 Flash Image-to-Text
Image-to-Text is designed for one visual input and a focused question. File Analysis is better suited to document-centered work that needs several related inputs and a more complete source-grounded response.