A long-horizon planning benchmark that tests whether agents can optimize under explicit time, budget, and feasibility constraints.
BenchLM mirrors the published score view for DeepPlanning. Qwen3.6 Plus leads the public snapshot at 41.5% , followed by Qwen3.5 397B (37.6%) and Claude Opus 4.5 (26.4%). BenchLM does not use these results to rank models overall.
Qwen3.6 Plus
Alibaba
Qwen3.5 397B
Alibaba
Claude Opus 4.5
Anthropic
The published DeepPlanning snapshot is tightly clustered at the top: Qwen3.6 Plus sits at 41.5%, while the third row is only 15.1 points behind. The broader top-10 spread is 27.1 points, so the benchmark still separates strong models even when the leaders cluster.
6 models have been evaluated on DeepPlanning. The benchmark falls in the Agentic category. This category carries a 22% weight in BenchLM.ai's overall scoring system. DeepPlanning is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
Year
2026
Tasks
Travel planning and constrained shopping
Format
Long-horizon planning benchmark
Difficulty
Constrained agent planning
DeepPlanning focuses on global constrained optimization rather than local next-step reasoning. It is useful because many agents can execute short actions but still fail when they must gather information and plan coherently over a long horizon under hard constraints.
Version
DeepPlanning 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 long-horizon planning benchmark that tests whether agents can optimize under explicit time, budget, and feasibility constraints.
Qwen3.6 Plus by Alibaba currently leads with a score of 41.5% on DeepPlanning.
6 AI models have been evaluated on DeepPlanning on BenchLM.
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