Benchmarking LLM Search APIs: Tavily vs Web Search Plus vs OpenAI Web Search

Modern AI applications—whether you’re building a research agent, chatbot, or a retrieval-augmented generation (RAG) pipeline—depend heavily on access to real-time, high-quality web data.

Over the past few months, tools like OpenAI’s web search and Tavily have emerged to fill this gap with structured, programmable search APIs. We’ve tested them extensively, and while they are powerful, we also saw room for improvement—in smartness, structure, and token efficiency.

So we built Web Search Plus. A developer-first, fast, and structured search API designed specifically for use in LLM applications.

🧠 The Problem with Traditional Search APIs
Most traditional search APIs can’t work alone — they only return a list of result links (URLs) without fetching or parsing the content. To actually get usable context, developers are forced to pair them with custom scraping logic.

This approach creates several major issues for LLM applications:

• 🔗 Only hyperlinks, no real content: Developers must build or integrate a crawler just to access the actual text behind each search result.
• 🧱 Raw full-page dumps: Scrapers often return the full HTML or raw article text — unfiltered, bloated, and noisy. Feeding this into your LLM bloats context windows, increases token cost, and risks hallucination.
• 🧩 Unstructured output: Even after crawling, the returned content lacks structure. Developers must manually extract the title, summary, source, date, etc., before formatting it for prompts.

✅ What LLMs actually need
Instead of dumping raw content, LLMs benefit from

• ✂️ Smaller Context: Only return search results tightly related to the query—irrelevant fluff is filtered out. Smaller context can reduce the cost of inference. And highly relevant context can effectively reduce hallucinations of LLMs.
• 🧾 Structured JSON: Fields like title, content(high-relevant part), and score allow predictable formatting for prompts and agents.
• 💰 Cost effective: In fact, the cost of searching is much higher than the cost of inference when LLM calls the web search tool. So the cost of searching must be reduced to an affordable level. That’s exactly what WebSearch.plus (https://websearch.plus) was built for.

🔎 Web Search Plus

• ✂️ Smaller Context: After getting the raw content of the web page, we didn’t directly return the raw content to LLM. We have developed our own hybrid search engine to filter out irrelevant information. Ensure that only highly relevant information is returned.
• 🧾 Structured JSON: Fields like title, content(high-relevant part), and score allow predictable formatting for prompts and agents. The developer can turn off the hybrid search too for returning the raw content.
• 💰 Cost effective: We did not adopt a per-request billing system. The developer just pays for tokens of search results. Usually, the cost is less than 1/10 of the cost of the OpenAI web search tool.

🧪The Benchmark: Tavily vs Exa vs Web Search Plus
We tested all three services across real-world queries:

What day did President Trump sign the Big Beautiful Bill?

And using GPT-4.1 as an LLM to infer the search result.

OpenAI Web Search

Search results

President Donald Trump signed the \”One Big Beautiful Bill Act\” into law on July 4, 2025, during an Independence Day ceremony at the White House. The event featured a military family picnic and a flyover by B-2 bombers, underscoring the significance of the occasion.

Cost

Input tokens: 312，$2.00 / 1M tokens，≈ $0.000636
Output tokens: 336，$8.00 / 1M tokens, ≈ $0.00268
Web Search request: $0.03 / request
Total: $0.033316

Obviously, Not only the cost of search is much high than cost of inference, but also the result of inference include signing location because the result of search include low-relevant info.

Web Search Plus

Search results

President Donald J. Trump signed the “One Big Beautiful Bill Act” (also called the Big Beautiful Bill) into law on July 4, 2025.

Cost

Input tokens: 64+918=1006，$2.00 / 1M tokens，≈ $0.001964
Output tokens: 22+35=57，$8.00 / 1M tokens, ≈ $0.000456
Web Search Plus tokens: 8691, $0.25 / 1M tokens ≈ 0.0027
Total: $0.00512

Total cost is just 1/6 of OpenAI. In addition, the cost of search is 1/10 of OpenAI web search tool.

Tavily (advanced)

Search result

President Donald J. Trump signed the “Big Beautiful Bill,” also referred to as The One Big Beautiful Bill, into law on July 4, 2025

Cost

Input tokens: 63+1928=1991，$2.00 / 1M tokens，≈ $0.003982
Output tokens: 23+97=120，$8.00 / 1M tokens, ≈ $0.00096
Tavily Search: $0.016 / 1 request = $0.016
Total: $0.020942

Total cost is only 37% less than OpenAI and almost 4x the cost of Web Search Plus. The cost of search is 50% of the OpenAI web search tool. And the number of search result tokens is 2x the web search plus (1928 vs 918).

In summary
In the same inference result, the cost of Web Search Plus is much lower than OpenAI and Tavily. Get 4M tokens for free; no credit card required, just sign in.