reasoning benchmark
Seal-0 is a benchmark for evaluating agentic search capabilities.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score57.40% | Percentile100.00% | Participants6 | EvidenceC | Evaluated |
| Rank02 | ModelMA | Score56.30% | Percentile80.00% | Participants6 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score47.20% | Percentile60.00% | Participants6 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score46.90% | Percentile40.00% | Participants6 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score44.10% | Percentile20.00% | Participants6 | EvidenceC | Evaluated |
| Rank06 | ModelAC | Score41.40% | Percentile0.00% | Participants6 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What Seal-0 measures and how its scores work.
Seal-0 is a reasoning benchmark that evaluates agentic search capabilities.
It measures models' ability to navigate and retrieve information using tools, with the metric Score reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Seal-0.
Kimi K2.5 is currently ranked first with 57.40%.
The current leaders are Kimi K2.5 (57.40%), Kimi K2-Thinking-0905 (56.30%), and Qwen3.5-27B (47.20%).
Qwen3.5-35B-A3B has the lowest matched official input price at $0.25 input / $2.0 output per 1M tokens.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures models' ability to navigate and retrieve information using tools, with the metric Score reported as a ratio.
Yes. Higher values rank better for this benchmark.
6 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis seal-0 AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Kimi K2.5 currently leads Seal-0 with 57.40%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.