Try Gemini 3.7 Flash API for file analysis, document review, extraction, summaries, and multimodal reasoning through Flaq AI's stable Google API access.
Related Gemini 3.7 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-3.7-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-3.7-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-3.7-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 3.7 Flash File Analysis Pricing
| Parameters | Price | Original Price | Discount |
|---|
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Fast and Affordable Gemini 3.7 Flash File Analysis API
Gemini 3.7 Flash File Analysis API is Flaq AI's cost-effective route for turning uploaded source material into useful text. It uses the latest Gemini Flash model for complex, multi-step analysis while keeping the product surface focused on file-centered questions, optional visual context, and generated text. Teams can use this API to organize source material, extract relevant information, and draft reviewable findings from a bounded set of files in one coherent request.
Key Features of Gemini 3.7 Flash File Analysis API
-
Advanced File-Grounded Reasoning: Analyze supplied files through detailed instructions and receive structured text that reflects the source material and requested task.
-
Very Affordable Analysis Workflow: Use a cost-effective Flash configuration for document-centered automation, review queues, research assistance, and knowledge operations.
-
Multi-File Context: Bring a related set of uploaded files into one request so the model can support comparison, synthesis, extraction, and cross-source questions.
-
Optional Visual Context: Pair file inputs with a supported image when visual context helps interpret the source material or clarify the task.
-
Controlled API Responses: Use structured conversational messages, output-length controls, and the documented response mode to fit product and operational workflows.
-
Review-Ready Text Output: Return summaries, findings, draft answers, and organized notes that can move into human approval or downstream automation.
How to Use Gemini 3.7 Flash File Analysis API on Flaq AI
-
Input: A supported set of files, a natural-language analysis instruction, and an optional image where relevant.
-
Output: Text-based summaries, extracted facts, comparisons, answers, and draft analysis based on the provided material.
-
Request Scope: Keep each request within the configured multi-file boundary and group only the source material that belongs to the same task.
-
Capabilities: File-grounded question answering, document summarization, cross-source synthesis, information extraction, and review-workflow assistance.
Best Use Cases for Gemini 3.7 Flash File Analysis API Integration
-
Complex Document Review: Prepare first-pass summaries, issue lists, and comparisons from related reports, specifications, proposals, and research material.
-
Research Operations: Organize multi-source findings, identify relevant evidence, and draft analytical notes for a researcher or editor to validate.
-
Internal Knowledge Work: Turn uploaded material into briefings, handoff notes, Q&A drafts, and structured information for teams.
-
Workflow Triage: Extract key details from submitted files and create review-ready records for support, operations, and content processes.
-
Productive File Assistants: Build application features that help users understand their own source material through focused, text-first analysis.
Note File Analysis is intended to support, not replace, expert review. Keep sensitive material protected, verify generated findings against the source files, and use appropriate human oversight for consequential decisions.
Gemini 3.7 Flash File Analysis API vs Competitors: Comparative Analysis
-
Gemini 3.7 Flash File Analysis vs. Gemini 3.6 Flash File Analysis
Gemini 3.6 Flash is a balanced option for document-centric workflows. Gemini 3.7 Flash is the newer Flash model for teams evaluating more advanced reasoning and multi-step synthesis on the same kind of file-centered task. -
Gemini 3.7 Flash File Analysis vs. GPT 5.6 Terra File Analysis
GPT 5.6 Terra file-analysis models provide a broad route for document applications. Gemini 3.7 Flash gives teams a highly cost-effective Gemini alternative for source-grounded extraction, review, and synthesis. -
Gemini 3.7 Flash File Analysis vs. Claude Document Analysis
Claude models are frequently evaluated for long-form document reasoning. Gemini 3.7 Flash offers another capable option for applications that need structured analysis from a bounded set of uploaded files. -
Gemini 3.7 Flash File Analysis vs. Gemini 3.7 Flash Image-to-Text
Image-to-Text is designed for one visual input and a focused question. File Analysis is the appropriate route when the task requires document-centered context and several related files. -
Gemini 3.7 Flash File Analysis vs. Gemini 3.7 Flash Text-to-Text
Text-to-Text is best for instructions and chat without attachments. File Analysis adds Flaq AI's configured file-input workflow when responses need to be grounded in uploaded source material.