Free to try Kimi K3 Text-to-Text API for long-horizon coding, reasoning, knowledge work, and scalable agent workflows through stable Flaq AI access for teams.
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: 'kimi-k3-text-to-text',
messages: [
{
role: 'user',
content: 'Write a concise Java beginner tutorial outline.'
}
],
stream: true,
max_tokens: 2000,
top_k: 50
})
});
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': 'kimi-k3-text-to-text',
'messages': [
{
'role': 'user',
'content': 'Write a concise Java beginner tutorial outline.',
}
],
'stream': True,
'max_tokens': 2000,
'top_k': 50,
},
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": "kimi-k3-text-to-text",
"messages": [
{
"role": "user",
"content": "Write a concise Java beginner tutorial outline."
}
],
"stream": true,
"max_tokens": 2000,
"top_k": 50
}'
Kimi K3 Text to Text Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Kimi K3 Text-to-Text API for Long-Horizon Coding & AI Agents
Kimi K3 Text-to-Text API brings Moonshot AI's flagship reasoning model to developers through a production-ready text API on Flaq AI. Designed for long-horizon coding, complex knowledge work, and multi-step reasoning, Kimi K3 can follow detailed instructions, work through extended technical context, and produce structured responses for demanding applications. Flaq AI provides a straightforward chat-completions integration for teams building coding assistants, research workflows, and text-based agents at scale.
Key Features of Kimi K3 Text-to-Text API
-
Long-Horizon Coding Intelligence: Work through complex engineering tasks, codebase analysis, debugging, refactoring, and implementation planning with a model designed for sustained technical reasoning.
-
Advanced Reasoning for Knowledge Work: Analyze dense information, connect evidence across long inputs, and develop coherent answers for research, strategy, documentation, and professional decision support.
-
Extended Context Understanding: Process large bodies of text and maintain continuity across detailed conversations, specifications, technical documents, and multi-stage tasks.
-
Reliable Instruction Following: Use clear natural-language directions to control objectives, response structure, depth, tone, and output format for repeatable application workflows.
-
Flexible Text Generation Controls: Adjust response length and sampling behavior to balance concise answers, deeper analysis, and application-specific output requirements.
-
Scalable Chat API Integration: Connect Kimi K3 to existing assistants, internal tools, and automated text pipelines through Flaq AI's unified chat-completions endpoint.
How to Use Kimi K3 Text-to-Text API on Flaq AI
-
Input: Structured text messages containing user requests, instructions, conversation history, code, or reference material.
-
Output: Natural-language text responses for reasoning, coding, analysis, writing, and other text-based workflows.
-
Generation Controls: Configure response length and token sampling, and choose streaming or non-streaming delivery according to your application experience.
-
API Route: Send requests through the Flaq AI chat-completions endpoint using kimi-k3-text-to-text as the model identifier.
-
Capabilities: Long-horizon coding, complex reasoning, knowledge synthesis, structured writing, technical analysis, and multi-turn text interaction.
Best Use Cases for Kimi K3 API Integration
-
Coding Assistants & Engineering Agents: Build developer tools that can investigate code, explain failures, plan changes, and support longer implementation tasks across multiple steps.
-
Large-Context Codebase Analysis: Review architecture, trace behavior across files, summarize unfamiliar repositories, and prepare migration or refactoring plans from extensive technical context.
-
Research & Knowledge Synthesis: Turn long reports, source material, and internal documentation into organized findings, comparisons, summaries, and decision-ready briefs.
-
Complex Business Workflows: Support planning, policy analysis, operational documentation, and multi-stage reasoning tasks that require consistent context and structured responses.
-
Technical Writing & Documentation: Generate specifications, implementation guides, API explanations, test plans, and clear developer-facing documentation from detailed requirements.
Note Please ensure messages and generated content comply with applicable safety, privacy, and data-handling requirements. This Flaq AI model route accepts text messages; do not assume that multimodal inputs or Kimi product-side tools are available through this endpoint.
Kimi K3 vs Competitors: Comparative Analysis
-
Kimi K3 vs. Kimi 2.7
Kimi 2.7 remains suitable for established conversational and coding workflows. Kimi K3 is positioned for more demanding long-horizon coding, deeper knowledge work, and sustained multi-step reasoning when tasks require stronger overall capability. -
Kimi K3 vs. Claude Fable 5
Claude Fable 5 targets premium proprietary reasoning and agentic development workflows. Kimi K3 offers a powerful Moonshot AI alternative with open-model foundations, extended-context strengths, and practical API access for teams evaluating different production stacks. -
Kimi K3 vs. GPT 5.6 Sol
GPT 5.6 Sol provides a broad proprietary ecosystem for advanced coding and general-purpose agents. Kimi K3 differentiates through Moonshot AI's long-horizon coding focus and gives developers another scalable option for complex text-based engineering and knowledge workflows. -
Kimi K3 vs. GLM 5.2
GLM 5.2 is a competitive general-purpose model for reasoning and developer applications. Kimi K3 emphasizes sustained execution across large technical contexts, making it especially relevant for codebase-scale analysis, extended planning, and demanding knowledge tasks.