LOCA-Bench (256k)
LOCA-Bench is a long-context agentic benchmark. The 256k variant evaluates agents using the official ReAct mode with an environment description length of 256k tokens, measuring how well models reason and act over very long contexts.
MiniMax M3 from MiniMax currently leads the LOCA-Bench (256k) leaderboard with a score of 0.493 across 1 evaluated AI models.
What LOCA-Bench (256k) measures
LOCA-Bench (256k) is a text benchmark that evaluates large language models on reasoning and agents tasks. LLM Stats tracks 1 model on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.5, with the leader reaching 0.5.
Compare leaders on the best AI for reasoning and best AI for agents leaderboards.
MiniMax M3 leads with 49.3%.
Progress Over Time
Interactive timeline showing model performance evolution on LOCA-Bench (256k)
LOCA-Bench (256k) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  MiniMax M3New MiniMax | — | 1.0M | $0.60 / $2.40 |
FAQ
Common questions about LOCA-Bench (256k).
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