Perplexity AI Context Window, Token Limits, and Memory: model capacities and operational structure

Perplexity AI integrates large-context reasoning, retrieval-augmented search, and session-based memory across its different models and platforms. While users often focus on its conversational depth, the system is governed by defined token and context limits that vary by model and environment. Understanding how these limits work—along with how Perplexity manages session memory and enterprise repositories—is essential for optimizing performance in both consumer and API workflows.

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Context windows across Perplexity models.

Perplexity operates multiple models under the Sonar family, each with its own context window, defining the total number of tokens that can be processed in a single request.

• Sonar: Supports a 128,000-token context window.

• Sonar Pro: Extends this capacity to 200,000 tokens, the largest currently available in the platform.

• Sonar Deep Research: Maintains a 128,000-token context window while adding advanced reasoning and citation tracking.

The context window determines how much input text (including conversation history and file content) the model can retain for reasoning. When the window is filled, older parts of the conversation are truncated or summarized internally.

While Perplexity’s app-level features may appear to handle even larger inputs—sometimes advertised as approaching 1 million tokens—these extended capacities result from segmented retrieval and routing, not a single model window. The system dynamically retrieves relevant context across indexed chunks, effectively emulating a longer session without increasing the core model limit.

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Token limits in the app and API.

The size of a prompt is constrained not only by the model window but also by file and message handling rules.

• Pasted text limit: In the Perplexity web and mobile apps, direct text input can include roughly 8,000 tokens per query. When larger text is pasted, the system automatically converts it into a file upload.

• File uploads: Both the app and API accept document formats such as PDF, DOC, DOCX, TXT, and RTF, up to 50 MB per file. Each file is pre-processed into textual segments that are passed to the model for analysis.

• API integration: Developers using the Perplexity API can call Sonar, Sonar Pro, or Sonar Deep Research endpoints with full access to their respective 128K or 200K context limits. Each call includes both prompt and response tokens in its total token count.

These token limits directly affect cost and latency. Larger retrieval contexts and reasoning operations in Deep Research mode consume additional “reasoning” and “citation” tokens, which are metered separately in the API pricing structure.

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How token usage scales with retrieval size.

Perplexity distinguishes between context window and search context size. The first defines how much data the model can process, while the second determines how much external content is retrieved from the web or user repositories.

When users adjust the search context setting (low, medium, or high), Perplexity retrieves more documents for grounding but does not increase the model’s intrinsic window. This means that increasing the search depth raises token usage and cost but does not change the 128K or 200K hard limit.

For developers and enterprise users, tuning retrieval breadth is critical to balancing speed, accuracy, and expense. Narrow search contexts improve performance on structured prompts, while high-context retrieval yields more comprehensive answers at higher token consumption.

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Memory within sessions and across conversations.

Perplexity employs a multi-layered memory architecture that differentiates between thread memory, cross-thread memory, and enterprise repositories.

1. Thread memory (session-level).Every conversation, or “thread,” retains its history until it reaches the model’s context window. Within that window, the model remembers everything previously said, allowing it to respond with continuity. Anonymous threads remain active for 14 days before deletion.

2. Cross-thread memory (profile-level).Signed-in users can enable a persistent Memory feature that allows the system to remember user preferences, interests, and factual details between conversations. These memories are visible and editable in the user settings, giving full control over what Perplexity retains.

3. Enterprise repositories (organizational memory).Enterprise accounts include long-term “knowledge memory” through persistent file repositories.

   • Enterprise Pro users can store up to 5,000 files.

   • Enterprise Max users can store up to 10,000 files.

   • Each account supports up to 500 file uploads per day, with a per-file limit of 50 MB.

These repositories serve as durable context sources that can be referenced by the model without re-uploading, effectively functioning as a persistent private corpus.

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Table — Perplexity context and memory limits.

Environment or Feature	Limit or Behavior	Description
Sonar (API)	128,000 tokens	Standard model for reasoning and chat
Sonar Pro (API)	200,000 tokens	Extended context for advanced analysis
Sonar Deep Research	128,000 tokens	Adds reasoning and citation tokens
App — text input	~8,000 tokens per query	Converts longer text into files automatically
File uploads	≤ 50 MB per file	PDF, DOCX, TXT, RTF, DOC formats
Anonymous thread retention	14 days	Session deleted after expiration
Cross-thread memory	Persistent (opt-in)	Remembers preferences and recurring facts
Enterprise repository	5,000–10,000 files	Long-term storage for corporate documents

This table summarizes the operational boundaries that define Perplexity’s data handling and memory behavior.

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Developer-level memory implementation.

At the API level, developers can emulate memory beyond a single context window through summary buffering and vector storage. This involves periodically summarizing the conversation and reinjecting the condensed representation into future requests.

Common patterns include:

• Rolling summary buffer: Retain short summaries of previous turns, replacing full text when nearing the window limit.

• Vector-based retrieval memory: Store message embeddings externally and retrieve the most relevant pieces for each new prompt.

• Hybrid strategy: Combine buffer summaries for immediate continuity with vector retrieval for deeper historical recall.

These techniques ensure continuity across thousands of exchanges without exceeding the token limit of Sonar or Sonar Pro.

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Operational recommendations.

• Use Sonar Pro for large analytical or research sessions that require 150K–200K tokens per query.

• For day-to-day reasoning, Sonar or Deep Research provides a balance between performance and token cost.

• When using the app, upload documents instead of pasting long text; the file pipeline ensures complete ingestion.

• Enable Memory only for recurring use cases where personalization is useful, as it retains user-level data.

• For teams and organizations, leverage enterprise repositories as a persistent knowledge memory to centralize file storage and reduce repeated uploads.

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Summary of Perplexity’s memory and token architecture.

Perplexity’s performance depends on a hierarchy of limits and memory systems. The Sonar family defines strict context windows—128K tokens for standard models and 200K for Pro—while the app enforces smaller per-query caps for pasted text. Long-term continuity is supported through session memory, cross-thread personalization, and enterprise repositories that act as persistent knowledge bases.

For developers, the best results come from explicit memory management using summaries or vector stores, ensuring stability and continuity across long projects. Together, these mechanisms allow Perplexity to maintain conversational consistency, manage massive documents, and scale from consumer use to enterprise-level reasoning.

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