A broad research-agent benchmark for open-ended information gathering, synthesis, and answer construction across wide search spaces.
BenchLM mirrors the published score view for WideResearch. Kimi K2.6 leads the public snapshot at 80.8% , followed by Claude Opus 4.5 (76.4%) and Qwen3.6 Plus (74.3%). BenchLM does not use these results to rank models overall.
Kimi K2.6
Moonshot AI
Claude Opus 4.5
Anthropic
Qwen3.6 Plus
Alibaba
The published WideResearch snapshot is tightly clustered at the top: Kimi K2.6 sits at 80.8%, while the third row is only 6.5 points behind. The broader top-10 spread is 20.7 points, so the benchmark still separates strong models even when the leaders cluster.
7 models have been evaluated on WideResearch. The benchmark falls in the Agentic category. This category carries a 22% weight in BenchLM.ai's overall scoring system. WideResearch is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
Year
2026
Tasks
Open-ended research tasks
Format
Multi-source research evaluation
Difficulty
Broad research-agent workflows
WideResearch evaluates whether a model can sustain a research process over multiple sources and branches rather than answering from shallow retrieval. BenchLM tracks it as a display-only browsing and synthesis benchmark.
Version
WideResearch 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.
A broad research-agent benchmark for open-ended information gathering, synthesis, and answer construction across wide search spaces.
Kimi K2.6 by Moonshot AI currently leads with a score of 80.8% on WideResearch.
7 AI models have been evaluated on WideResearch on BenchLM.
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