Perplexity's agent, Computer, leverages a "multi-model orchestration" strategy. For a single user request, it might use Opus for planning, GPT for writing, and Gemini for audio. This model-agnostic approach allows it to always use the best-in-class model for each sub-task, a flexibility its larger competitors lack.

Unlike other LLMs that handle one deep research task at a time, Manus can run multiple searches in parallel. This allows a user to, for example, generate detailed reports on numerous distinct topics simultaneously, making it incredibly efficient for large-scale analysis.

Instead of relying on one powerful model for all tasks, the leading strategy is 'smart routing'—using a panel of models and directing each task to the most appropriate one. This compound architecture demonstrably beats single frontier models on both cost and performance.

Different LLMs have unique strengths and knowledge gaps. Instead of relying on one model, an "LLM Council" approach queries multiple models (e.g., Claude, Gemini) for the same prompt and then uses an agent to aggregate and synthesize the responses into one superior output.

The belief that a single, god-level foundation model would dominate has proven false. Horowitz points to successful AI applications like Cursor, which uses 13 different models. This shows that value lies in the complex orchestration and design at the application layer, not just in having the largest single model.

Instead of relying on a single "best" foundation model, the winning strategy will be creating "harnesses" that combine multiple models. This approach leverages the unique, exponential advantages of each lab—for instance, using Google's Gemini for multimodal tasks and Anthropic's Claude for code generation.

Move beyond single LLMs to autonomous agents like Manus. These "digital employees" can execute complex, multi-step projects by autonomously selecting and weaving together the best models and tools (e.g., Gemini for video analysis, others for PDF generation) for each sub-task.

Unlike single-provider tools, Perplexity Computer orchestrates multiple AI models (Sonnet, Gemini, Opus) for different sub-tasks like planning, coding, and reasoning. This ensemble approach reduces the frustrating re-prompting loop and yields better results from a single initial prompt.

Powerful AI tools are becoming aggregators like Manus, which intelligently select the best underlying model for a specific task—research, data visualization, or coding. This multi-model approach enables a seamless workflow within a single thread, outperforming systems reliant on one general-purpose model.