This reporting page isolates the web research slice of agentic performance. It prioritizes sourced benchmarks for browsing, evidence gathering, and multi-step web task completion rather than generic overall agent scores.
This page ranks models using only sourced web research benchmarks in the reporting family.
Bottom line: Web research agents need to browse, gather evidence, and synthesize findings. BrowseComp is the most predictive benchmark here.
According to BenchLM.ai, GPT-5.5 Pro leads this ranking with a score of 90.1, followed by GPT-5.4 Pro (89.3) and Claude Mythos Preview (86.9). There is meaningful separation between the top models, suggesting genuine performance differences.
The best open-weight option is DeepSeek V4 Pro (Max) (ranked #6 with a score of 83.4). While proprietary models lead, open-weight options are within striking distance for teams willing to trade a few points of performance for full model control.
This ranking is based on provisional overall weighted scores across BenchLM.ai's scoring formula tracked by BenchLM.ai. For detailed model profiles, click any model name below. To compare two specific models head-to-head, use the "vs #" links.
GPT-5.5 Pro
OpenAI · 1M
GPT-5.4 Pro
OpenAI · 1.05M
Claude Mythos Preview
Anthropic · 1M
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The top model on this sourced reporting-family slice is GPT-5.5 Pro by OpenAI with an average of 90.1.
The best open-weight model is DeepSeek V4 Pro (Max) at position #6.
21 models are listed with sourced benchmark coverage in this reporting family.
This ranking averages sourced web-research benchmarks. It isolates the browsing and evidence-gathering slice of agentic performance.
Web research benchmarks test specific browsing patterns. Real-world web research also depends on access to search APIs, page rendering quality, and anti-bot measures that benchmarks do not capture.
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