reasoning benchmark
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.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
LOCA-Bench (256k) Ranking
Higher score ranks better on this benchmark.
| 01 | MI | 49.3% | 100.0% | 1 | C |
LOCA-Bench (256k) Highlights
The leading models and scores on this benchmark.
What is LOCA-Bench (256k)?
What LOCA-Bench (256k) measures and how its scores work.
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.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- LOCA-Bench (256k)
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- loca-bench-256k|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about LOCA-Bench (256k).
Which model scores highest on LOCA-Bench (256k)?
MiniMax M3 is currently ranked first with 49.3%.
What does LOCA-Bench (256k) measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.