An agentic browsing benchmark where models search the web, gather evidence, and answer list-style questions using browser tools.
BenchLM mirrors the published score view for DeepSearchQA. Kimi K2.6 leads the public snapshot at 92.5% , followed by Kimi K2.5 (77.1%) and Muse Spark (74.8%). BenchLM does not use these results to rank models overall.
Kimi K2.6
Moonshot AI
Kimi K2.5
Moonshot AI
Muse Spark
Meta
The published DeepSearchQA snapshot is tightly clustered at the top: Kimi K2.6 sits at 92.5%, while the third row is only 17.7 points behind. The broader top-10 spread is 29.7 points, so the benchmark still separates strong models even when the leaders cluster.
7 models have been evaluated on DeepSearchQA. The benchmark falls in the Agentic category. This category carries a 22% weight in BenchLM.ai's overall scoring system. DeepSearchQA is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
Year
2026
Tasks
Agentic browsing and list-answer questions
Format
Search / open / find browser-agent evaluation
Difficulty
Agentic web research
Meta describes DeepSearchQA as a browser-tool evaluation graded with an F1-style semantic set match. BenchLM stores it as a display-only agentic search benchmark.
Version
DeepSearchQA 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
An agentic browsing benchmark where models search the web, gather evidence, and answer list-style questions using browser tools.
Kimi K2.6 by Moonshot AI currently leads with a score of 92.5% on DeepSearchQA.
7 AI models have been evaluated on DeepSearchQA on BenchLM.
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