Claude Code Guide
Set up Flaq AI Claude models and explore Claude Code skills
Free to try Claude Opus 5 API by Anthropic for text reasoning, writing, coding, planning, and complex analysis through Flaq AI's unified API. Built for developers.
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: 'claude-opus-5-text-to-text',
messages: [
{
role: 'user',
content: 'Draft a technical decision memo for this architecture choice.'
}
],
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': 'claude-opus-5-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Draft a technical decision memo for this architecture choice.',
}
],
'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": "claude-opus-5-text-to-text",
"messages": [
{
"role": "user",
"content": "Draft a technical decision memo for this architecture choice."
}
],
"stream": true,
"max_tokens": 2048
}'
| Parameters | Price | Original Price | Discount |
|---|
Claude Opus 5 Text-to-Text API is designed for demanding reasoning, analysis, writing, coding, and structured language workflows. It accepts text-based conversations and gives applications a dependable model endpoint for multi-turn products, automation, and knowledge work on Flaq AI.
Advanced Language Reasoning: Work through complex questions, trade-offs, and multi-step problems with structured natural-language responses.
Technical and Editorial Writing: Draft, revise, summarize, and transform content while preserving the requested tone and organization.
Multi-Turn Context: Maintain relevant conversation history for assistants, research tools, and long-running language workflows.
Structured Message Integration: Connect text prompts and conversation context to existing chat-completion style application flows.
Flexible Generation Controls: Adjust the available response-length and sampling controls for different product experiences.
Clear Output Synthesis: Turn large instructions and scattered context into organized explanations, plans, and decision-ready text.
Input: Text prompts, system guidance, user instructions, and relevant conversation context.
Conversation Design: Keep instructions and supporting context organized so the model can follow the intended task hierarchy.
Output: Structured text responses returned through the Flaq AI chat workflow for display, automation, or review.
Generation Controls: Set the available response-length and sampling parameters according to the required output style.
Application Handling: Validate the response, handle provider or rate-limit errors, and preserve context when continuing the conversation.
Technical Analysis: Explain architecture, evaluate implementation options, and produce engineering documentation.
Long-Form Writing: Draft articles, reports, briefs, and other content that requires consistent structure and revision.
Research Synthesis: Organize complex source material into summaries, comparisons, and decision-support documents.
Coding Assistance: Generate, review, refactor, and explain code within an application development workflow.
Conversational Products: Power assistants and knowledge tools that need clear reasoning across multiple turns.
Note Model responses should be reviewed for factual accuracy, safety, and suitability before they are used in high-impact or customer-facing workflows.
Claude Opus 5 vs. GPT-5: GPT-5 offers broad reasoning and multimodal capabilities. Claude Opus 5 is a strong option for teams prioritizing careful language work, structured analysis, and editable long-form output.
Claude Opus 5 vs. Gemini 2.5 Pro: Gemini 2.5 Pro combines reasoning with a broad Google ecosystem. Claude Opus 5 focuses this comparison on text-first analysis, writing, and conversation workflows.
Claude Opus 5 vs. Qwen 3.8 Max: Qwen 3.8 Max provides capable language reasoning and Alibaba-oriented integration. Claude Opus 5 offers a distinct Anthropic-centered path for careful synthesis and technical communication.
Claude Opus 5 vs. GLM 5.2: GLM 5.2 supports structured text conversations and application-friendly controls. Claude Opus 5 is aimed at workflows where reasoning depth and writing quality are central requirements.
Claude Opus 5 vs. DeepSeek V3: DeepSeek V3 is widely used for cost-conscious language and coding workflows. Claude Opus 5 is positioned for teams that value polished analysis and reliable editorial output.
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