Perplexity uses a multi-model system powered by its own Sonar model—built on Llama 3.1 70B—alongside advanced LLMs such as GPT-5.1, Claude 4.5, Gemini 3 Pro, Grok 4.1, and Kimi K2. Instead of relying on a single model, Perplexity routes each query to the model best suited for search, reasoning, coding, or multimodal tasks. This combination enables faster retrieval, more accurate citations, and deeper reasoning than any single LLM alone.
Even with Perplexity’s built-in model switching, it still isn’t enough for many users who also need tools for different situations. That raises a practical question: is there a single place to access top models without moving across platforms?
GlobalGPT addresses that gap by combining 100+ AI models—including GPT-5.1, Claude 4.5, Sora 2 Pro, Veo 3.1, and real-time search models—inside a single interface, making it easier to test, compare, and use different LLMs without maintaining multiple subscriptions, all starting at around $5.75.
What LLMPowers Perplexityin 2025?
Perplexity uses a coordinated multi-model system rather than a single AI model. The platform evaluates your query, identifies its intent, and routes it to the LLM most capable of producing an accurate, source-backed, or reasoning-heavy response. Key points include:
- Perplexity runs multiple LLMs simultaneously, not one model behind the scenes.
- Sonar handles real-time search, retrieval, summarization, and ranking.
- GPT-5.1, Claude 4.5, Gemini 3 Pro, Grok 4.1, and Kimi K2 handle advanced reasoning, coding, multimodal prompts, or trend-sensitive tasks.
- The multi-model architecture improves factual accuracy, because different LLMs excel at different tasks.
- Routing is intent-aware, meaning Perplexity interprets whether the request is search, reasoning, coding, or creative.
- This approach reduces hallucinations compared to single-model chatbots.
| Model Name | Provider | Specialty | Key Strengths | Typical Query Types |
| Sonar (Llama 3.1 70B–based) | Perplexity | Real-time retrieval & search ranking | Fast citation generation, high freshness, reliable factual grounding | News queries, fact-checking, up-to-date research, multi-source synthesis |
| pplx-7b-online | Perplexity (finetuned from Mistral-7B) | Lightweight online LLM with web snippets | High freshness, accurate short answers, fast responses | Quick factual lookups, trending topics, time-sensitive queries |
| pplx-70b-online | Perplexity (finetuned from Llama2-70B) | Heavyweight online LLM with deeper reasoning | High factuality, strong holistic responses, reduced hallucinations | Complex factual prompts, fresh datasets, technical lookups |
| GPT-5.1 | OpenAI | Deep reasoning & structured generation | Strong logic, high coding ability, long-context performance | Essays, multi-step reasoning, code debugging, structured planning |
| Claude 4.5 |
What Is Perplexity’s Default Model and What Does It Actually Do?
Perplexity’s default model is not GPT, Claude, or Sonar. It is a lightweight, speed-optimized model designed for quick browsing and short retrieval tasks. It exists to deliver fast first-pass answers for low-complexity prompts.
Key characteristics:
- Optimized for speed rather than deep reasoning.
- Used primarily in the free plan or for simple queries.
- Triggers minimal computation, reducing latency.
- Switches automatically to Sonar when a query requires citations or multiple sources.
- Less capable in complex reasoning, coding, or multi-step explanations.
- Designed to reduce load on heavier models while keeping the experience smooth.
Deep Dive into Sonar: Perplexity’s Real-TimeSearch Engine
Sonar is Perplexity’s primary engine for retrieval. Built on Llama 3.1 70B, it is fine-tuned to read, rank, and synthesize information from multiple webpages in real time.
Why Sonar matters:
- Purpose-built for retrieval, not just text generation.
- Reads dozens of webpages in parallel, then aggregates evidence.
- Provides citations automatically, improving trust and transparency.
- Switches into reasoning mode for multi-step or ambiguous queries.
- Outperforms GPT and Claude on fresh information, especially news or evolving topics.
- Delivers fast search responses, often within milliseconds.
- Improves factual grounding, reducing hallucination risk.
Full List of LLMsPerplexityUses Across Subscription Plans
Beyond Sonar and the default model, Perplexity integrates several top-tier LLMs. Each serves a specific purpose:
GPT-5.1 (OpenAI)
- Excellent for long-form reasoning
- Strong coding and debugging
- Good at structured planning
- Lower hallucination rate vs older models
Claude 4.5 Sonnet (Anthropic)
- Highly stable step-by-step reasoning
- Great for math, logic, and code clarity
- Efficient with long input contexts
Claude 4.5 Opus (Max plans only)
- Deepest reasoning abilities
- Best for technical, multi-step explanations
- Slower but most precise
Gemini 3 Pro (Google)
- Best multimodal understanding
- strong image/video reasoning
- Great for code writing and analysis
Grok 4.1 (xAI)
- Best for real-time, trend-sensitive queries
- Excellent conversational flow
Kimi K2 (Moonshot)
- Privacy-oriented
- Good for careful, step-by-step reasoning
Why Perplexity uses all these models
- Different tasks require different strengths
- Specialized LLMs outperform general-purpose ones
- Routing improves output quality and robustness
How Perplexity’s “Best Mode” Chooses the Right LLM
Perplexity analyzes your query to determine which model produces the best answer.
Routing factors include:
- Is the question factual or research-based? → Sonar
- Does it require deep reasoning? → GPT-5.1 or Claude
- Is the query trending or social-media–related? → Grok
- Does it involve images or multimodal elements? → Gemini
- Is privacy a concern? → Kimi K2
- Does the prompt require citations? → Sonar
Additional behavior:
- Reasoning Mode toggle increases depth of GPT/Claude
- Search Mode forces Sonar
- Pro Search expands retrieval scope and sources
Side-by-Side Comparison: PerplexityLLMsand Their Ideal Uses
Perplexity’s LLMs specialize in different tasks. Here’s how they compare:
- Best for factual accuracy: Sonar
- Best for complex reasoning: GPT-5.1
- Best for logical clarity: Claude 4.5
- Best for multimodal tasks: Gemini 3 Pro
- Best for real-time context: Grok 4.1
- Best for privacy-sensitive prompts: Kimi K2
- Best for everyday mixed-use: Best Mode auto-routing
Perplexityvs ChatGPTvs Claude vs Gemini
Although Perplexity uses many of the same underlying models, its architecture differs:
- Perplexity excels at:
- fact retrieval
- multi-source synthesis
- citation-backed answers
- fast news summarization
- ChatGPT excels at:
- creative writing
- extended reasoning sequences
- structured planning
- Claude excels at:
- coding
- math
- logical analysis
- Gemini excels at:
- image + video interpretation
- multimodal workflows
When to Use Each Model Inside Perplexity
Practical guidance:
- Use Sonar when you need fact-based answers, citations, or real-time info.
- Use GPT-5.1 for logic-heavy essays, explanations, and multi-step reasoning.
- Use Claude 4.5 for coding tasks, math proofs, and structured analysis.
- Use Gemini 3 Pro for image-related tasks or video understanding.
- Use Grok 4.1 for trending topics, social media insights, or conversational tasks.
- Use Kimi K2 when privacy or careful reasoning is needed.
Real Examples of PerplexityModel Switching
Examples of Perplexity’s automatic routing:
- Breaking news query → Sonar (fast retrieval + citations)
- Debugging Python code → Claude 4.5 or GPT-5.1
- Identifying an image → Gemini 3 Pro
- Looking up a trending meme → Grok 4.1
- Long logical decomposition → GPT-5.1 or Claude Opus
Pricing Tiers and LLM Access
| Tier | Models Included | Key Limitations |
| Free | – Default Model (varies by load) – Limited Sonar access | – No Sonar Large – Rate limits – No advanced file uploads – No API credits |
| Pro | – Sonar Small – Sonar Large – pplx-7b-online / pplx-70b-online (via Labs) | – Still limited for heavy workflows – No guaranteed peak-time performance for some models – Monthly cap on API credits |
| Enterprise / Teams | – Custom model routing – Full Sonar stack – pplx-online family – Dedicated infra options | – Requires contract – Pricing varies – Integration work needed |
What each plan includes:
- Free Plan:
- Default model
- Limited Sonar
- No GPT/Claude/Gemini access
- Pro Plan:
- Sonar
- GPT-5.1
- Claude 4.5 Sonnet
- Gemini 3 Pro
- Grok 4.1
- Kimi K2
- Max Plan:
- All Pro models
- Claude 4.5 Opus
- Additional retrieval depth
H2: Limitations of Perplexity’s Multi-Model System
Despite its strengths, Perplexity has constraints:
- Model availability varies by region
- No plugin ecosystem like ChatGPT
- Creative generation weaker than dedicated tools
- Some tasks still require manual fact-checking
- Routing is not always predictable
- Multimodal tasks remain less flexible than specialized platforms.
FAQAbout Perplexity’s LLMs
- Does Perplexity mainly use GPT? → No, it uses many models.
- Is Sonar better than GPT? → For retrieval tasks, yes.
- Can I force a specific model? → Only through Pro Search.
- Does Perplexity store data? → Per official docs, data use is limited and privacy-focused.
- Why do answers sound similar across models? → Shared training data and similar alignment methods.
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Final Thoughts on Perplexity’s Multi-Model Strategy
Perplexity’s multi-model architecture demonstrates how retrieval-first AI systems can outperform single-model chatbots on factual tasks, citations, and fast research.
For users whose workflows span multiple AI capabilities—search, reasoning, writing, and multimodal tasks—understanding these differences helps optimize output and tool selection. You can also compare how these models behave side by side using GlobalGPT, which brings many of the same top LLMs into one interface for easier evaluation.
