Web Search

Give your AI models access to real-time web data. Instead of relying on training data alone, models can search the web and ground their responses in current information.

What This Unlocks

Live web data — No more “I don’t know about recent events”
Current information — Break free from training cutoff dates
Automatic integration — Search results blend seamlessly into responses
Configurable depth — Control how much web context to include

How It Works

Add web_search_options to your API request. AnyAPI passes the search request to the model’s provider, which performs the web search and returns a response grounded in real-time data. The exact search mechanism depends on the provider — Anthropic uses its native web search tool, Google uses grounding with search, OpenAI uses its built-in search, and so on.

Chat Completions API

Basic Example

curl -X POST "https://api.anyapi.ai/v1/chat/completions" \
  -H "Authorization: Bearer your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-4",
    "messages": [
      {
        "role": "user",
        "content": "What are the latest developments in AI research this week?"
      }
    ],
    "web_search_options": {
      "search_context_size": "medium"
    }
  }'

import requests

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "anthropic/claude-sonnet-4",
        "messages": [
            {
                "role": "user",
                "content": "What are the latest developments in AI research this week?"
            }
        ],
        "web_search_options": {
            "search_context_size": "medium"
        }
    }
)

print(response.json()["choices"][0]["message"]["content"])

const response = await fetch('https://api.anyapi.ai/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer your_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'anthropic/claude-sonnet-4',
    messages: [
      {
        role: 'user',
        content: 'What are the latest developments in AI research this week?'
      }
    ],
    web_search_options: {
      search_context_size: 'medium'
    }
  })
});

const data = await response.json();
console.log(data.choices[0].message.content);

The `web_search_options` Parameter

Parameter	Type	Description
`search_context_size`	string	Amount of web context to include: `"low"`, `"medium"` (default), or `"high"`
`user_location`	object	Optional location for localized search results (supported by OpenAI, Anthropic, and Google models)

Localized Search with `user_location`

Pass a location to get region-specific search results:

Python

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "anthropic/claude-sonnet-4",
        "messages": [
            {"role": "user", "content": "What's the weather forecast for this weekend?"}
        ],
        "web_search_options": {
            "search_context_size": "low",
            "user_location": {
                "type": "approximate",
                "city": "San Francisco",
                "region": "California",
                "country": "US",
                "timezone": "America/Los_Angeles"
            }
        }
    }
)

Responses API

You can also use web search through the /v1/responses endpoint with the web_search_preview tool:

import requests

response = requests.post(
    "https://api.anyapi.ai/v1/responses",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "openai/gpt-5",
        "input": "What is happening in the stock market today?",
        "tools": [
            {
                "type": "web_search_preview",
                "search_context_size": "medium"
            }
        ]
    }
)

print(response.json())

const response = await fetch('https://api.anyapi.ai/v1/responses', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer your_api_key',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    model: 'openai/gpt-5',
    input: 'What is happening in the stock market today?',
    tools: [
      {
        type: 'web_search_preview',
        search_context_size: 'medium'
      }
    ]
  })
});

const data = await response.json();
console.log(data);

Search Context Levels

Level	Context	Speed	Best For
`"low"`	Minimal	Fastest	Quick fact-checks, simple questions
`"medium"`	Balanced	Moderate	News updates, general research
`"high"`	Maximum	Slowest	Deep analysis, complex research

Real-World Examples

Latest News

Python

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "google/gemini-2.5-flash",
        "messages": [
            {"role": "user", "content": "What are the top tech news stories today?"}
        ],
        "web_search_options": {
            "search_context_size": "medium"
        }
    }
)

Deep Research with Perplexity

Python

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "perplexity/sonar-pro",
        "messages": [
            {"role": "user", "content": "Analyze current trends in renewable energy investments"}
        ],
        "web_search_options": {
            "search_context_size": "high"
        }
    }
)

Reasoning Over Web Results

Python

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": "Bearer your_api_key",
        "Content-Type": "application/json"
    },
    json={
        "model": "openai/o3",
        "messages": [
            {"role": "user", "content": "Compare the latest quarterly earnings of NVIDIA vs AMD"}
        ],
        "web_search_options": {
            "search_context_size": "high"
        }
    }
)

Supported Models

Web search works with 800+ models on AnyAPI, including:

OpenAI: GPT-5, GPT-5 Mini, GPT-4o, GPT-4o Search Preview, o3-Pro, and more
Anthropic: Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5, and more
Google: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 3 Pro/Flash Preview, and more
xAI: Grok 4, Grok 4.1 Fast, Grok 3, and more
Perplexity: Sonar, Sonar Pro, Sonar Deep Research, Sonar Reasoning Pro
DeepSeek: DeepSeek V3.2, DeepSeek R1, and more
Qwen: Qwen3 Max, Qwen3 Coder Plus, Qwen3 235B, and more
Meta: Llama 4 Maverick, Llama 4 Scout, Llama 3.1 405B, and more
Mistral: Mistral Large, Codestral, Devstral, Magistral Small, and more

Check the model database for the full list of models with web search support.

Pricing

Web search incurs an additional per-search cost on top of regular token pricing. The exact cost depends on the model and provider.

Pro Tips

Match context to your needs: Quick questions = "low", research = "high"
Be specific in your prompts: “Tesla Q4 2025 earnings” beats “stock market”
Use with reasoning models: Models like o3, DeepSeek R1, and Claude with extended thinking can reason over web results for deeper analysis
Use Perplexity for research: Sonar models are built for search and return citations by default

Things to Keep in Mind

Slight latency: Web search adds a moment to response time
Cost scaling: Higher search_context_size uses more tokens and increases cost
Rate limits: Standard rate limits apply to requests with web search

Give your AI models internet access. Web search turns any model into a research assistant that knows what happened five minutes ago.

Get started

Features

Use Cases

Developer guides

Integrations

API Reference

What This Unlocks

How It Works

Chat Completions API

Basic Example

The `web_search_options` Parameter

Localized Search with `user_location`

Responses API

Search Context Levels

Real-World Examples

Latest News

Deep Research with Perplexity

Reasoning Over Web Results

Supported Models

Pricing

Pro Tips

Things to Keep in Mind

​What This Unlocks

​How It Works

​Chat Completions API

​Basic Example

​The web_search_options Parameter

​Localized Search with user_location

​Responses API

​Search Context Levels

​Real-World Examples

​Latest News

​Deep Research with Perplexity

​Reasoning Over Web Results

​Supported Models

​Pricing

​Pro Tips

​Things to Keep in Mind

What This Unlocks

How It Works

Chat Completions API

Basic Example

The `web_search_options` Parameter

Localized Search with `user_location`

Responses API

Search Context Levels

Real-World Examples

Latest News

Deep Research with Perplexity

Reasoning Over Web Results

Supported Models

Pricing

Pro Tips

Things to Keep in Mind