Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Try Grok 4.6 API by xAI for image-to-text, visual Q&A, OCR, analysis, and streaming multimodal responses through Flaq AI's stable unified API. Ideal for visual 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: 'grok-4.6-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);
}
}
}
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': 'grok-4.6-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)
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": "grok-4.6-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
}'
| Parameters | Price | Original Price | Discount |
|---|
Grok 4.6 Image-to-Text API combines xAI's reasoning-oriented text model with image understanding on Flaq AI. It lets applications send a focused visual input alongside an instruction, then receive text that explains, extracts, compares, or analyzes what the image contains. This Grok vision API integration is designed for workflows where visual evidence needs to become an actionable answer, technical observation, or structured next step.
Image-Grounded Analysis: Ask questions about a supplied image and receive text responses tied to visible details, layout, objects, and context.
Visual-to-Text Reasoning: Combine an image with natural-language instructions for explanation, comparison, troubleshooting, and analytical workflows.
Focused Image Input: Supply the route's configured image input with a clear task instruction, keeping image-question workflows straightforward to integrate.
Technical Inspection Assistance: Use visual context for interface review, screenshot interpretation, diagram explanation, product-feedback triage, and similar text-output tasks.
Controlled Conversational Requests: Pair image questions with structured message context so applications can provide task framing, prior requirements, and follow-up prompts.
Text-First Results: Receive generated text ready for review, display, routing, or use by a downstream workflow rather than treating the route as an image-generation endpoint.
Input: A supported image together with a natural-language request describing the question, extraction goal, or analysis task.
Output: Text responses that describe, explain, compare, or analyze the supplied visual content.
Image Delivery: Use the configured Flaq AI request format for the image input in your application workflow.
Capabilities: Screenshot analysis, visual question answering, image-grounded explanation, content review, and detail extraction.
Screenshot and UI Review: Explain interfaces, identify visible states, summarize feedback, or turn a screenshot into a developer-ready issue description.
Product and Catalog Assistance: Extract observable product details, generate attribute drafts, and support human review of visual submissions.
Technical Support Triage: Help teams interpret error screenshots, device photos, or setup images before routing a case to an operator.
Document and Diagram Explanation: Convert charts, diagrams, slides, and visual references into clear textual observations and follow-up questions.
Content Operations: Support moderation queues, accessibility drafting, and image-description workflows where humans retain final review.
Note Image understanding results should be reviewed before use in high-impact, safety-critical, legal, medical, or financial decisions. Do not rely on generated text as a substitute for expert inspection.
Grok 4.6 Image-to-Text vs. Grok 4.5 Image-to-Text
Grok 4.5 provides an established option for visual
question-answering workflows. Grok 4.6 is the newer model generation for teams evaluating stronger reasoning around
image-grounded requests.
Grok 4.6 Image-to-Text vs. Gemini 3.7 Flash Image-to-Text
Gemini 3.7 Flash offers an efficient multimodal model
family. Grok 4.6 Image-to-Text provides a focused Flaq route for teams that want to test xAI visual reasoning in a
visual-input, text-output workflow.
Grok 4.6 Image-to-Text vs. GPT 5.6 Terra Image-to-Text
GPT 5.6 Terra visual models are commonly used for general multimodal tasks.
Grok 4.6 is a useful alternative when the workflow values xAI's reasoning and coding-oriented model family alongside
visual analysis.
Grok 4.6 Image-to-Text vs. Claude Vision
Claude vision models can be well suited to explanation and
document-oriented tasks. Grok 4.6 Image-to-Text gives developers another API option for image-grounded questions,
reviews, and operational workflows.
Grok 4.6 Image-to-Text vs. Gemini 3.6 Flash Image-to-Text
Gemini 3.6 Flash balances broad multimodal capability
with efficient operation. Grok 4.6 is a strong candidate to evaluate when visual requests also require detailed
technical reasoning in text.
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