MCP Benchmark: Top MCP Servers for Web Access in 2026
We ran 4 distinct tasks 5 times each across 8 cloud-based MCP servers, then stress-tested the infrastructure with 250 concurrent AI agents.
Explore the success rates for web search, extraction, and browser automation across 8 providers. Speed benchmarks showing the 7-182 second range for task completion. Scalability scores from 250-agent stress tests. Pricing comparisons per task. Real limitations each provider faces with bot detection and JavaScript rendering.
MCP servers with web access capabilities
Product | Success rate for web
search and extract | Success rate for
browser automation | Web search and
extraction speed (s) | Browser automation
speed (s) | Scalability score |
|---|---|---|---|---|---|
100% | 90% | 30 | 30 | 77% | |
78% | 0% | 32 | N/A | 19% | |
75% | N/A | 14 | N/A | 54% | |
Nimble | 93% | N/A | 16 | N/A | 51% |
Firecrawl | 83% | N/A | 7 | N/A | 65% |
Hyperbrowser | 63% | 90% | 118 | 93 | N/A |
Browserbase | 48% | 5% | 51 | 104 | N/A |
Tavily | 38% | N/A | 14 | N/A | 45% |
Exa | 23% | N/A | 15 | N/A | N/A |
*Web search & extraction tasks are run with Bright Data’s default MCP server, browser automation tasks are run with Bright Data MCP Pro Mode, since the tools needed for browser automation are available on the Pro Mode.
**The table is sorted by scores in the web search & extraction category, with sponsors displayed at the top.
Each of the dimensions above and their measurement methods are outlined below:
Success rate of MCP servers in web access
*N/A indicates that the MCP server does not have this capability.
We run web search and extraction alongside browser automation only four providers offer both. Bright Data completed 100% of web search & extraction tasks. For browser automation, Bright Data (Pro Mode) and Hyperbrowser both achieved 90% completion rates.
Across all benchmarked tools, just four handle the dual requirements for web agents:
Web search & extraction means searching the web and following links across pages to collect data.
Browser automation means interacting with JavaScript elements to fill forms and trigger actions.
Task details appear in the methodology section below.
Speed
Completion times for successful tasks, with only failed attempts returning error messages, aren’t comparable to actual completions.
Web search & extraction: Firecrawl averaged 7 seconds with 83% accuracy, making it the fastest. Firecrawl added support for the FIRE-1 model and HTTP Server-Side Events in January 2026, enabling real-time data streaming and improved handling of JavaScript-heavy pages with interaction barriers.1
Browser automation: Bright Data averaged 30 seconds with 90% accuracy. Bright Data now offers a free tier with 5,000 MCP requests per month for the first 3 months, covering web search and Web Unlocker scraping.2
Our navigation dataset comprised 80 data points (8 providers, 2 tasks, 5 repetitions per task). Results show a negative correlation between success rates and speed:
Why speed and success trade off:
- Websites flag bots as suspicious traffic and trigger anti-scraping features.
- Some MCP servers fail immediately when detected.
Servers that bypass detection use unblocking technology, adding 4+ seconds per request.
Scalability
Our benchmark measures performance under high concurrent load. The X-axis shows single-agent success rates from web search & extraction tests. The Y-axis shows scalability scores from 250-agent stress tests measuring server stability.
Built each agent on the LangChain create_react_agent framework with gpt-4.1-nano-2025-04-14. Agents received e-commerce prompts like “Go to target.com, find a throw pillow under 20 dollars.” Success required navigating the site, finding a matching product, and returning structured JSON (URL, price, rating) within 5 minutes.
Results from concurrent testing:
- Bright Data emerged as the overall leader, achieving the highest success rate at 76.8% with a competitive average completion time of 48.7 seconds per successful task.
- Firecrawl achieved a success rate of 64.8% and an average task duration of 77.6 seconds.
- Oxylabs demonstrated the fastest performance, completing its tasks in an average of 31.7 seconds while maintaining a solid success rate of 54.4%.
- Nimble recorded a 51.2% success rate, but its successful tasks took significantly longer, averaging 182.3 seconds to complete.
- Tavily completed the tasks with a success rate of 45%, with the second fastest average completion time of 41.3 seconds.
- Apify completed the test with a lower success rate of 18.8%, though its successful tasks were relatively quick, averaging 45.9 seconds.
Potential reasons behind the performance differences
Anti-bot detection and unblocking
Bright Data and Hyperbrowser achieve 90-100% success rates by mimicking human behavior to bypass bot-detection systems. This advanced evasion adds processing time, which explains why Bright Data averages 30 seconds for browser automation, compared with competitors’ faster completions.
Firecrawl prioritizes speed (7 seconds average) but achieves only 83% success rate, suggesting lighter anti-bot measures.
Oxylabs demonstrates the fastest scalability (31.7 seconds) with a moderate 54.4% success rate—a balanced approach that doesn’t fully address all anti-scraping challenges.
Architecture: full-stack vs. specialized
Full-stack providers (Apify, Bright Data, Browserbase, Hyperbrowser) run headless Chrome instances, enabling form filling, JavaScript execution, and complex interactions. This requires more infrastructure but handles interactive elements.
Specialized extractors (Exa, Firecrawl, Nimble, Oxylabs, Tavily) use lighter-weight scraping methods that run faster but cannot handle forms or JavaScript-dependent content.
Infrastructure under concurrent load
Bright Data’s 76.8% success rate with fewer than 250 concurrent agents demonstrates a distributed infrastructure designed for enterprise-scale dedicated proxy networks and load-balancing systems that maintain performance.
Nimble’s 182.3-second average completion time, despite 51.2% success rate, indicates bottlenecks when handling concurrent requests, likely from queuing mechanisms or limited proxy pool capacity.
Apify’s dramatic drop to an 18.8% success rate in scalability tests (compared with higher single-agent performance) suggests the infrastructure is optimized for use cases other than high-concurrency agent workloads.
Strategic focus
Speed-optimized: Firecrawl and Oxylabs deliver 7-31 second completions for high-volume, low-complexity tasks where some failures are acceptable.
Reliability-focused: Bright Data and Hyperbrowser achieve 90-100% success rates for critical workflows where accuracy matters more than processing time.
Balanced: Tavily targets the middle with 45% success rates and 41.3-second completions, neither extreme speed nor maximum reliability.
Resource allocation and pricing
- Higher success rates require larger investments:
- Larger proxy networks to rotate IPs and avoid detection
- More sophisticated fingerprinting technologies
- Longer retry mechanisms and fallback strategies
- Enhanced JavaScript rendering capabilities
Methodology to assess the MCP servers’ web access capabilities
Integrated MCPs into LangGraph agent framework using langchain-mcp-adapters. Four prompts tested distinct capabilities:
Web Search & Extraction:
- AI SDR for lead generation: “Go to LinkedIn, find 2 people who work at AIMultiple, provide their names and profile URLs.”
- Shopping assistant: “Go to Amazon and find 3 headphones under $30. Provide their names, ratings and URLs.”
Browser Automation:
- Travel assistant: “Find the best price for the Betsy Hotel, South Beach, Miami on June 16, 2025. Provide the price and URL.”
- Form filler: “Go to https://aimultiple.com/, enter my email xxx@aimultiple.com in the newsletter subscription, and click subscribe.”
Executed each task 5 times per agent. Each task contributed equally tothe total score, with points for successfully retrieving each required data element. Code tracked both the MCP tool execution time and the complete agent processing duration.
Used identical agents with identical prompts and system prompts across all tests. System prompts were written in a universal language without specific tool mentions or detailed instructions. This ensures a fair comparison, though writing custom code for each agent could achieve a 100% success rate.
The first three tasks measured search and extraction; the last task measured browser automation.
Features
We have also measured several key metrics for these MCP servers. For an explanation of features, please see the methodology section in agent browser benchmark.
Search engine support
Targeting
Security
Data security is crucial for enterprise operations. We checked whether the companies of these agent browsers had data security certification. All of the companies claim on their websites to have either an ISO 27001 or a SOC 2 certification.
Pricing benchmark
Comparing pricing across MCP servers is difficult, as each uses different parameters (per API call, per GB, per page).
We measured the price per single task. Since most providers don’t break down costs granularly over time, we chose:
- First task for web search & extraction benchmark (highest overall success rate)
- Last task for browser automation benchmark
Most products offer various plans with different limits, some allowing additional credit purchases.
Critical note: LLM costs exceeded browsing costs during these tasks. Our Claude Sonnet 3.5 usage cost more than any MCP server pricing. LLM pricing matters more than MCP server pricing when building web agents.
*Prices may vary depending on the selected plan and enterprise discounts.
Participants
We included all MCP servers that provide cloud-based web browsing capabilities:
- Apify
- Bright Data
- Browserbase
- Exa
- Firecrawl
- Hyperbrowser
- Nimble
- Oxylabs
- Tavily
Apify, Bright Data, and Oxylabs are sponsors of AIMultiple.
For this version of our benchmark, we excluded MCP servers that run on users’ own devices, as they have limited capacity to handle a high volume of requests. If we missed any cloud-based MCP servers with web browsing capabilities, please let us know in the comments.
MCP web browsing challenges & mitigations
When configured in an MCP client such as Claude Desktop, LLMs can leverage specialized MCP servers. Web access MCPs are particularly valuable as they enable web data extraction, including the ability to render JavaScript-heavy pages, bypass common access restrictions, take actions, fill forms and access geo-restricted content from various global locations, but they come with some challenges.
While we faced similar challenges to the agent browser benchmark, MCPs present novel challenges to benchmarking. LLMs, with the addition of an external memory function, can be used as a Turing machine, and with an MCP server that provides browsing capabilities, it is theoretically possible to complete any web navigation or browser automation task with MCP servers that provide these capabilities.
Therefore, by writing custom code for each agent, it is possible to achieve 100% success rates. However, that is not a good proxy for MCP users who want to provide simple instructions and achieve high success rates. Therefore, we chose prompts that are as simple and as universal as possible and do not make references to functionality in specific MCP servers.
Context window
The context window may be exceeded in long tasks. Agents are consuming full pages as they navigate the web, and as a result, the limited context window of LLMs is sooner or later exceeded. Therefore, to build agents that complete tasks that involve many pages, users need
- LLMs with large context windows
- Optimize the sizes of the pages passed to the LLM. For example, you may be able to programmatically remove unnecessary parts of pages and have LLM focus only on the important parts of the pages.
Developer experience
Experienced developers can use MCP servers on MCP clients that require coding, and can easily run parallel tests or use MCP code execution. Also, no-code MCP clients like Claude or Cursor can be used easily with no developer experience required.
FAQ
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.