Web Search Integration
Retrieves live web results at inference time, providing grounded answers with citations. Delivers approximately 2x more citations per response compared to the standard Sonar model.
Sonar Pro is a search-augmented text generation model developed by Perplexity, designed to handle complex research queries that require thorough source attribution and multi-step reasoning. It operates with a 200,000-token context window, allowing it to process large volumes of information within a single session. The model supports both text and image inputs and can produce up to 8,192 output tokens per response. It also includes function calling, structured output generation, and a reasoning mode for analytical tasks. Sonar Pro is Perplexity's premium tier offering within the Sonar model family, delivering approximately twice the citations and search results compared to the standard Sonar model. This makes it particularly well-suited for enterprise applications, professional research workflows, and use cases that demand comprehensive source coverage and reliable multi-step query handling. The model's training data extends through March 2025, and its live web search integration means responses can draw on current information beyond that date. It is available via API for developers building research-intensive or knowledge-heavy applications.
High-signal model metadata in a structured two-column overview table.
The entity that provides this model.
The routed model identifier exposed by upstream providers.
The number of tokens supported by the input context window.
The number of tokens that can be generated by the model in a single request.
Whether the model's code is available for public use.
When the model was first released.
When the model's knowledge was last updated.
The providers that offer this model. This is not an exhaustive list.
Types of data this model can process.
A fuller summary of positioning, capabilities, and source-specific details for Sonar Pro.
Sonar Pro is a search-augmented text generation model developed by Perplexity, designed to handle complex research queries that require thorough source attribution and multi-step reasoning. It operates with a 200,000-token context window, allowing it to process large volumes of information within a single session. The model supports both text and image inputs and can produce up to 8,192 output tokens per response. It also includes function calling, structured output generation, and a reasoning mode for analytical tasks.
Sonar Pro is Perplexity's premium tier offering within the Sonar model family, delivering approximately twice the citations and search results compared to the standard Sonar model. This makes it particularly well-suited for enterprise applications, professional research workflows, and use cases that demand comprehensive source coverage and reliable multi-step query handling. The model's training data extends through March 2025, and its live web search integration means responses can draw on current information beyond that date. It is available via API for developers building research-intensive or knowledge-heavy applications.
Retrieves live web results at inference time, providing grounded answers with citations. Delivers approximately 2x more citations per response compared to the standard Sonar model.
Supports a 200,000-token context window, enabling processing of lengthy documents or extended multi-turn conversations in a single session.
Includes a reasoning mode that breaks down complex queries into sub-tasks, gathers information from multiple angles, and synthesizes a coherent response.
Accepts image inputs alongside text, allowing the model to interpret and reason over visual content as part of a query.
Supports structured output generation and function calling, making it suitable for integration into programmatic workflows and agent pipelines.
Includes built-in content moderation support to help filter and manage outputs in safety-sensitive deployment contexts.
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Additional usage-cost dimensions synced into the project for this model.
Places where this model is available, based on the synced detail-page metadata.
Endpoint-level provider data currently available for this model.
The configurable options currently documented for this model.
Determines whether or not a request to an online model should return citations.
Determines whether or not a request to an online model should return images.
Parameters currently listed by OpenRouter or the local catalog for this model.
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
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AIME 2024
American math olympiad problems
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
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HLE
Questions that challenge frontier models across many domains
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LiveCodeBench
Real-world coding tasks from recent competitions
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MATH-500
Undergraduate and competition-level math problems
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MMLU-Pro
Expert knowledge across 14 academic disciplines
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SciCode
Scientific research coding and numerical methods
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Official model cards, release notes, docs, and other references synced from the source page.
Sonar Pro discussions are most active in r/perplexity_ai, r/steelseries, r/mcp. The strongest match in this snapshot has 122 upvotes and 13 comments.
Hey `r/perplexity_ai`,
I've been working on a fun personal project called **MuseWeb**, a small Go server that generates entire web pages live using an AI model. My goal was to test how different models handle a complex, creative task: building a coherent and aesthetically pleasing website from just a set of text-based prompts.
After testing various local models, I connected it to the Perplexity API to try out the Sonar models. I have to say, I was genuinely blown away by the quality. The `sonar-pro` model, in particular, produces incredibly elegant, well-structured, and creative pages. It has a real knack for design and for following the detailed instructions in my system prompt.
Since this community appreciates the "how" behind the "what," I wanted to share the project and the prompts I'm using. I just pushed a new version (1.0.7) with a few bug fixes, so it's a great time to try it out.
**GitHub Repo:** [https://github.com/kekePower/museweb](https://github.com/kekePower/museweb)
---
### **The Recipe: How to Get Great Results with Sonar**
The magic is all in the prompts. I feed the model a very strict "brand guide" and then a simple instruction for each page. The server automatically maps a file like `about.txt` to the URL `/?prompt=about`.
**For those who want a deep dive into the entire prompt engineering process**—including the iterations, the bugs we fixed, and our findings—I've written up a detailed document here:
**[MuseWeb Prompt Engineering Deep Dive](https://github.com/kekePower/museweb/blob/main/museweb-prompt-engineering.md)**
For a quick look, here is a snippet of the core `system_prompt.txt` that defines the rules:
```
You are The Brand Custodian, a specialized AI front-end developer. Your sole purpose is to build and maintain the official website for a specific, predefined company. You must ensure that every piece of content, every design choice, and every interaction you create is perfectly aligned with the detailed brand identity and lore provided below. Your goal is consistency and faithful representation.
---
### 1. THE CLIENT: Terranexa (A Fictional Eco-Tech Company)
* **Mission:** To create self-sustaining ecosystems by harmonizing technology with nature.
* **Core Principles:** 1. Symbiotic Design, 2. Radical Transparency, 3. Long-Term Resilience.
---
### 2. MANDATORY STRUCTURAL RULES
* A single, fixed navigation bar at the top of the viewport.
* MUST contain these 5 links in order: Home, Our Technology, Sustainability, About Us, Contact. The `href` for these links must point to the prompt names, e.g., `<a href="/?prompt=home">Home</a>`, `<a href="/?prompt=technology">Our Technology</a>`, etc. The server automatically handles the root path `/` as the home page.
* If a footer exists, the copyright year MUST be **2025**.
---
### 3. TECHNICAL & CREATIVE DIRECTIVES
* Your entire response **MUST** be a single HTML file.
* You **MUST NOT** link to any external CSS or JS files. All styles MUST be in a `<style>` tag.
* You **MUST NOT** use any Markdown syntax. Use proper HTML tags for all formatting.
```
---
### **How to Try It Yourself with Perplexity**
MuseWeb is designed to be easy to run. You just need Go installed.
**1. Clone and Build:**
```bash
git clone https://github.com/kekePower/museweb.git
cd museweb
go build .
```
**2. Configure for Perplexity:**
Copy `config.example.yaml` to `config.yaml` and set it up for the Perplexity API.
```yaml
# config.yaml
server:
port: "8080"
prompts_dir: "./prompts"
model:
backend: "openai" # Perplexity uses an OpenAI-compatible API
name: "sonar-large-32k-chat" # Or "sonar-small-32k-online", etc.
openai:
api_key: "pplx-YOUR_PERPLEXITY_API_KEY" # Get one from your Perplexity account
api_base: "https://api.perplexity.ai"
```
**3. Run It!**
```bash
./museweb
```
Now open `http://localhost:8080` and see what Sonar creates!
I'm super impressed with how well Perplexity's models handle this task. It really shows off their creative and instruction-following capabilities beyond just being a great search/answer engine.
I'd love to hear your thoughts or if you give it a try with other Sonar models. Happy to answer any questions
Just came across this. Has anyone ever used this sonar. I'm an avid pond hopper and have always wanted to see what's in those ponds. Yes I know it's expensive and that I'd have to get a subscription. Besides that, is it a good picture, good quality, stuff like that
This thing is definitely not ready for prime time but it’s starting to come along.
The thought behind all this is a SLR basically has two shutters but one is a moving mirror.
So why not mount the SX-70 Sonar shutter in front of another shutter and then sync them up like the moving mirror? I guess it works!
There’s another video in comments.
Hey everyone,
I'd like to buy the WC Stealth Pads for my Nova Pro Wireless.
According to YouTube, you're supposed to adjust the EQ after changing the ear pads because the sound changes. That makes sense.
However, I also use Sonar with different profiles depending on the game (Battlefield, COD, Arc Raiders).
So, what do I do if I have the Stealth Pads and want to use a Sonar profile for a specific game?
Do I have to combine both EQ settings to get the best gaming experience? What's the correct way to do this?
I strongly suspect that the individual profiles in Sonar are designed for the original ear pads.
Sonar Pro supports a 200,000-token context window, allowing it to process large documents or long multi-turn conversations within a single session.
Sonar Pro can generate up to 8,192 output tokens per response.
The model's training data extends through March 2025. However, because Sonar Pro integrates live web search, it can also surface information published after that date.
Yes, Sonar Pro supports both text and image inputs, enabling the model to interpret visual content as part of a query.
Sonar Pro is Perplexity's premium tier and delivers approximately twice the citations and search results per response compared to the standard Sonar model. It also includes a reasoning mode and supports more complex, multi-step queries.
Yes, Sonar Pro supports function calling and structured output generation, making it compatible with agent-based and programmatic integration workflows.
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