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https://magai.co/Magai: One Platform for 50+ AI Models
Magai is a unified AI workspace that consolidates access to over 50 artificial intelligence models—including ChatGPT, Claude, Gemini, DeepSeek, and Perplexity—within a single interface. Rather than juggling separate subscriptions or restarting conversations when switching models, Magai allows users to change AI engines mid-chat while preserving full conversation history, context, and instructions. This eliminates the friction of model hopping and enables seamless workflows where different models handle different tasks: for example, using ChatGPT for brainstorming, Claude for simplifying technical explanations, and DeepSeek for narrative storytelling—all without losing thread continuity.
The platform emphasizes persistent context across model switches, a feature highlighted as unique compared to other tools that force users to begin anew when changing AI engines. Users can start a conversation with one model, switch to another for refinement or specialization, and continue as if no transition occurred. This capability supports complex, multi-stage tasks such as research, drafting, editing, and formatting within a single chat environment.
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Try ToolSuite NowBeyond model flexibility, Magai integrates directly with over 130 business applications spanning email, calendars, document storage, project management, communication, marketing, CRM, and social media. Once connected, these integrations become available to any AI model in the system, allowing users to command AI to interact with their existing tools—such as drafting emails via GPT, updating project status in Asana, or analyzing spreadsheet data—without leaving the chat. The platform also supports scheduled tasks, enabling users to automate recurring AI-driven actions (e.g., generating weekly reports or summarizing meeting notes) across all connected integrations, with no coding required.
Magai includes a built-in document editor that lets users draft, edit, and export content as PDF or DOCX files directly from the chat interface. Additional productivity tools include Prompt Enhance, which automatically refines vague inputs into structured, effective prompts; a searchable Prompt Library for saving and reusing high-performing instructions; and Real-Time Edits, allowing users to modify both their input and the AI’s output mid-conversation without restarting. For collaborative workflows, team members can be invited into live chats instantly, with view-only sharing via secure public links and unified file access ensuring everyone works from the same context.
The platform addresses common limitations of standalone AI services by eliminating cooldowns and rate limits. Magai promises uninterrupted generation during high-output periods, with instant top-ups available to add more usage without changing plans or experiencing delays. This is positioned as a solution for users who hit usage caps on other platforms and need consistent, scalable performance.
Pricing is structured in three tiers: Standard at $20/month for individuals getting started, Pro at $40/month for serious users needing higher capacity, and Ultra at $200/month for power users requiring maximum monthly usage. All plans include access to all premium AI models, unlimited agents, and the full suite of integrations and tools. The Standard plan includes 100 scheduled task runs; Pro offers 1,000; Ultra provides 15x the Standard usage. Additional users can be added at $20/month each (Standard) or custom rates (Pro and Ultra). Magai also supports importing existing chat histories from ChatGPT and Claude via JSON export, allowing users to migrate their past conversations and resume work without downtime.
While Magai excels in model flexibility, integration depth, and team collaboration, it may not be ideal for users who only need a single, specialized AI model and prefer minimal interface complexity. Those seeking highly niche or experimental models not among the 50+ offered may find the selection limiting. Additionally, although the platform emphasizes ease of use, mastering the full range of features—such as conditional scheduling, context management, and cross-model instruction reuse—may require a learning curve for new users.
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