Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Try GPT 6.1 Sol API by OpenAI for writing, coding, conversation, and text analysis through Flaq AI's stable, affordable API with streaming responses for apps.
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: 'gpt-6.1-sol-text-to-text',
messages: [
{
role: 'user',
content: 'Write a concise product update for a developer audience.'
}
],
stream: true,
max_tokens: 2048
})
});
if (!response.ok) {
const errorBody = await response.json().catch(() => null);
throw new Error(errorBody?.error?.message ?? errorBody?.message ?? `HTTP ${response.status}`);
}
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(/\r?\n\r?\n/);
buffer = frames.pop() || '';
for (const frame of frames) {
const lines = frame.split(/\r?\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': 'gpt-6.1-sol-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Write a concise product update for a developer audience.',
}
],
'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": "gpt-6.1-sol-text-to-text",
"messages": [
{
"role": "user",
"content": "Write a concise product update for a developer audience."
}
],
"stream": true,
"max_tokens": 2048
}'
| Parameters | Price | Original Price | Discount |
|---|
GPT 6.1 Sol Text-to-Text API provides message-based language generation for writing tools, conversational products, and business workflows on Flaq AI. Send a task and its supporting text to request a draft, explanation, summary, or follow-up answer. With streaming delivery and an adjustable output token limit, developers can shape how generated text is returned and incorporate GPT 6.1 Sol into their application experience.
Prompt-Guided Writing: Specify the goal, audience, and desired structure to generate draft text that can be reviewed and refined inside your product.
Multi-Turn Conversations: Supply relevant message history to support follow-up questions and revisions within the same task.
Progressive Text Delivery: Stream responses into chat windows and editing interfaces as generation proceeds.
Output Length Control: Use an output token limit together with concise or detailed response instructions to manage the scope of an answer.
Text Transformation Workflows: Ask for summaries, reorganized notes, or rewrites of supplied material through the same message-based API.
Dedicated Text Route: Integrate text generation where the request does not require image input, document attachments, or web search.
Input: Text instructions, supporting passages, and relevant conversation messages.
Output: Generated text, delivered as a complete response or streamed incrementally.
Controls: An optional output token limit, streaming selection, and prompt-based guidance for tone and structure.
Capabilities: Drafting, rewriting, conversational question answering, summarization, and explanation of supplied text.
Product Writing Tools: Help users turn a rough brief into an outline or draft and then revise it through follow-up requests.
Customer Service Drafts: Combine a customer's question with approved product information to prepare a reply for review.
Team Summaries: Convert supplied notes into updates, meeting recaps, and next-step lists.
Interactive Learning: Offer explanations that users can explore further through conversation.
Application Text Automation: Generate recurring written outputs from the context supplied by your application.
Note Include the information needed to answer the request and verify factual output before publication. Select a dedicated image, file, or web-search route when those inputs are central to the task.
GPT 6.1 Sol Text-to-Text vs. Claude Sonnet 5.5 Text-to-Text Both offer text messages, streaming, and output length controls on Flaq AI. Test representative prompts to decide which responses best match your writing and conversation requirements.
GPT 6.1 Sol Text-to-Text vs. GPT 6.1 Sol Image-to-Text Image-to-Text adds visual input for image-based questions. The Text-to-Text route focuses on content already expressed in messages.
GPT 6.1 Sol Text-to-Text vs. GPT 6.1 Sol File Analysis File Analysis accepts uploaded documents for source-based review. Text-to-Text is suitable when the relevant excerpts are already included in the conversation.
GPT 6.1 Sol Text-to-Text vs. GPT 6.1 Sol Web Search Web Search is configured for online research. Text-to-Text does not enable web search and uses the task context supplied in messages.
Set up Flaq AI Claude models and explore Claude Code skills

Set up Flaq AI GPT models and explore Codex skills
Use Flaq AI LLM models in Hermes Agent
Use GLM 5.2, Kimi K3, and DeepSeek v4 in ZCode
Run DeepSeek Harness with Flaq AI DeepSeek models
Connect AI agents to Flaq image and video generation tools
Use GPT 6 Astra and Claude Fable 5.1 in WorkBuddy