BenchLM recommendation
Best AI Models for Web Research in 2026
As of July 23, 2026, the top model in best ai models for web research on the BenchLM leaderboard is GPT-5.6 Sol with a score of 92.2.
Last verified: July 23, 2026
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.
GPT-5.6 Sol leads this ranking with a score of 92.2, followed by Kimi K3 (91.2) and GPT-5.5 Pro (90.1). The top three are separated by just a few points — any of them would perform well for this use case.
The best open-weight option is MiniMax M3 (ranked #11 with a score of 83.5). Proprietary models hold a clear advantage in this category, though open-weight options may suffice for less demanding use cases.
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.
How to choose
Full Rankings (32 models)
Key Takeaways
The top model on this sourced reporting-family slice is GPT-5.6 Sol by OpenAI with an average of 92.2.
The best open-weight model is MiniMax M3 at position #11.
32 models are listed with sourced benchmark coverage in this reporting family.
Score in Context
What these scores mean
This ranking averages sourced web-research benchmarks. It isolates the browsing and evidence-gathering slice of agentic performance.
Known limitations
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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