GraphWalks
GraphWalks is a synthetic multi-hop long-context reasoning benchmark in which a model is given an edge-list representation of a graph and must traverse it to find neighboring nodes (via breadth-first search) or parent nodes for a given start node. Performance is reported as F1 of the model-predicted answer set versus the ground truth.
MAI-Thinking-1 from Microsoft currently leads the GraphWalks leaderboard with a score of 0.900 across 1 evaluated AI models.
What GraphWalks measures
GraphWalks is a text benchmark that evaluates large language models on long context and reasoning tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.9, with the leader reaching 0.9.
Compare leaders on the best AI for long context and best AI for reasoning leaderboards.
MAI-Thinking-1 leads with 90.0%.
Progress Over Time
Interactive timeline showing model performance evolution on GraphWalks
GraphWalks Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | Microsoft | 1.0T | — | — |
FAQ
Common questions about GraphWalks.
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