⚡ Universal Multi-Model AI Chat & Deep Reasoning API (Real-Time Web Citations & Zero Logs)

🚀 The #1 Most Complete, Low-Latency Privacy-First Conversational Engine, Multi-Model Routing, Web Search Citations & Technical Synthesis Scraper on Apify.

Looking for private, ultra-fast conversational AI and reasoning without logging, IP tracking, or paying expensive monthly per-seat enterprise fees?

The Universal Multi-Model AI Chat & Deep Reasoning API delivers sub-second conversational synthesis backed by 28+ leading AI foundation models (Qwen 3.5, Llama 3.1/3.3, GLM 4.7 Flash, DeepSeek V3.2, Claude 4.6 Sonnet, GPT-5.5, Mistral Large) with real-time web search citation extraction, conversation memory (conversation_id), and zero user profiling.

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🌟 Institutional Comparison: Why Choose Universal Multi-Model AI Chat?

Feature	ChatGPT Plus ($20/mo)	Claude Pro ($20/mo)	Perplexity Pro ($20/mo)	⚡ Universal AI Chat Actor
Pricing Model	💸 $20.00 / user / mo	💸 $20.00 / user / mo	💸 $20.00 / user / mo	⚡ Pay-Per-Event ($0.003 / query)
Privacy & Anonymity	❌ Full User Tracking	❌ Account Required	⚠️ History Logged	✅ Zero Data Retention / No Logs
Multi-Model Access	⚠️ GPT models only	⚠️ Claude models only	⚠️ Fixed models	✅ 28+ Models (Meta, Alibaba, Z.ai, Anthropic)
Real-Time Web Search	✅ Yes	⚠️ Limited	✅ Yes	✅ Live Web Search Synthesis
Conversation Memory	✅ Yes	✅ Yes	✅ Yes	✅ Stateful conversation_id Support
Latency / Response Time	~1.5s	~2.0s	~1.8s	⚡ Sub-800ms Edge Gateway

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⚡ Comprehensive Feature Breakdown

1. 🤖 Multi-Model Foundation Selector (model)

Choose from 28+ top foundation models across Meta (Llama 3.1, Llama 3.3, Llama 4), Alibaba (Qwen 3.5 122B, Qwen 3 Coder 480B), Anthropic (Claude 4.6 Sonnet, Claude 4.5 Haiku, Claude Opus 4.8), DeepSeek, OpenAI (GPT-5.5, GPT OSS), xAI (Grok 4.3), and Mistral AI.

2. 💬 General Conversational Chat (CHAT)

Execute real-time conversational question answering on any general, historical, technical, or creative topic with zero IP retention and dynamic model selection.

3. 🧠 Deep Multistep Reasoning (REASONING)

Tackle complex multi-variable logic problems, financial derivations, statistical breakdowns, and architectural trade-offs.

4. 💻 Code Generation & Technical Synthesis (CODE_GEN)

Generate production-ready code in Python, JavaScript, TypeScript, Go, Rust, C++, and SQL with inline documentation and algorithmic optimizations.

5. 📋 Dynamic Models Directory (MODELS)

Query the live registry of 28+ supported foundation models with capability flags, token context limits, and provider metadata.

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📥 Input Parameters & Schema

Parameter	Type	Required	Default	Options / Valid Values	Description
model	string	No	automatic	automatic, qwen-3-235b, llama-3-8b-instruct, glm-4-7-flash, deepseek-v3-2, claude-3-sonnet, mistral-large, qwen-3-coder-480b	AI Model Architecture: Select your preferred reasoning engine.
mode	string	No	CHAT	CHAT, REASONING, CODE_GEN, MODELS	Primary Operation Mode: Selects the task routing pipeline.
query	string	Yes	—	Any search query, question, or prompt	The prompt or task to process.
conversation_id	string	No	null	Any string identifier (e.g. user-chat-101)	Conversation ID: Maintains context across multiple turns.
system_language	string	No	en_US	en_US, en_GB, es_ES, fr_FR, de_DE, it_IT, pt_BR, ja_JP, zh_CN, zh_TW, ar_SA, ru_RU, hi_IN, ko_KR, nl_NL, tr_TR, pl_PL, sv_SE, id_ID, vi_VN	Output Language Locale Dropdown: 20+ supported language locales.

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📥 Input Examples

Example 1: Multi-Turn Conversational Q&A

{
  "model": "automatic",
  "mode": "CHAT",
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points.",
  "conversation_id": "session-101",
  "system_language": "en_US"
}

Example 2: High-Performance Python Code Generation

{
  "model": "qwen-3-235b",
  "mode": "CODE_GEN",
  "query": "Write a high-throughput Python async web crawler using aiohttp with connection pooling and retry logic.",
  "system_language": "en_US"
}

Example 3: Analytical Reasoning & Mathematics

{
  "model": "glm-4-7-flash",
  "mode": "REASONING",
  "query": "Derive the Black-Scholes partial differential equation and explain the financial intuition behind delta hedging.",
  "system_language": "en_US"
}

Example 4: List Available Models Directory

{
  "mode": "MODELS"
}

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📤 Output Dataset Example

{
  "mode": "CHAT_RESULT",
  "success": true,
  "conversation_id": "session-101",
  "turn_count": 1,
  "query": "Explain how quantum computing differs from classical computing in 3 clear bullet points.",
  "model": "qwen-3-235b",
  "answer": "1. Basic Unit of Information: Classical computers use bits (0 or 1), whereas quantum computers use qubits, which can exist simultaneously as 0 and 1 via quantum superposition.\n2. Computational Scaling & Parallelism: Thanks to quantum entanglement and superposition, quantum computers can evaluate exponentially large solution spaces simultaneously rather than calculating sequentially.\n3. Problem Domain Optimization: Classical computers excel at everyday sequential computing, while quantum systems are built for complex simulations like molecular modeling, cryptography, and combinatorial optimization.",
  "citations_count": 0,
  "citations": [],
  "usage": {
    "completion_tokens": 142,
    "prompt_tokens": 28,
    "total_tokens": 170
  }
}

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💻 Multi-Language Code Integration Suite

Python (apify-client):

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "model": "automatic",
    "mode": "CHAT",
    "query": "What are the latest breakthroughs in fusion energy in 2026?",
    "conversation_id": "user-chat-99",
    "system_language": "en_US"
}

run = client.actor("dev00/brave-leo-ai-chat-api").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['model']}] Answer:\n{item['answer']}")

JavaScript / Node.js (apify-client):

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const input = {
  model: "qwen-3-235b",
  mode: "CODE_GEN",
  query: "Write a high-performance Redis cache layer in TypeScript.",
  conversation_id: "dev-session-42",
  system_language: "en_US"
};

const run = await client.actor("dev00/brave-leo-ai-chat-api").call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log("Generated Code:", items[0].answer);

cURL:

curl -X POST "https://api.apify.com/v2/acts/dev00~brave-leo-ai-chat-api/runs?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"model": "automatic", "mode": "CHAT", "query": "Explain General Relativity in simple terms.", "conversation_id": "session-1"}'

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💰 Pay-Per-Event (PPR) Pricing Model

This Actor operates under the transparent Pay-Per-Event monetization model:

• Price: $0.003 per AI chat / reasoning execution ($3.00 per 1,000 queries).
• No Monthly Minimums: Only pay for the exact queries you run.
• Includes 28+ Foundation Models: Zero price difference between Qwen, Llama, GLM, and Claude models.

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🎯 High-Growth Industry Use Cases

1. AI Customer Support Agents: Embed private, low-latency conversational AI without storing customer PII.
2. Automated Code Assistants & Review Bots: Generate optimized snippets and review pull requests programmatically.
3. Academic & Research Synthesis: Perform literature summaries and mathematical derivations.
4. Market & Competitive Intelligence: Extract real-time factual summaries across industry sectors.
5. Multi-Agent Systems: Deploy fast, cost-effective reasoning workers across AutoGPT, CrewAI, and LangChain pipelines.

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❓ Frequently Asked Questions (FAQ)

Q: How does this API ensure privacy?

All requests are routed with anonymized headers and zero session tracking or query logging.

Q: What models are supported?

Over 28 foundation models including Qwen 3.5 122B, Llama 3.1 8B, Llama 3.3 70B, Claude 4.6 Sonnet, GLM 4.7 Flash, DeepSeek V3.2, and GPT-5.5.

Q: How does conversation history work?

Simply pass a conversation_id string across multiple requests, and the engine automatically tracks and appends conversation context across turns.

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