# NanoBEIR Multilingual Extended (NanoBEIR Multilingual)

> A display-only multilingual retrieval benchmark reported by Liquid AI for LFM2.5 retriever models, using NDCG@10 across 11 languages.

Canonical page: https://benchlm.ai/benchmarks/nanobeirmultilingual

- Category: [Multilingual](/multilingual)
- Last updated: September 27, 2026

## About NanoBEIR Multilingual

- Year: 2026
- Tasks: Multilingual document retrieval
- Format: NDCG@10 average
- Difficulty: Multilingual retrieval
- Paper: [LFM2.5 Retrievers: Bi-directional LFMs for Fast Multilingual Search](https://www.liquid.ai/blog/lfm2-5-retrievers)

Liquid reports average NanoBEIR Multilingual Extended NDCG@10 across Arabic, German, English, Spanish, French, Italian, Japanese, Korean, Norwegian, Portuguese, and Swedish. BenchLM stores the average as a display-only retrieval signal.

NanoBEIR Multilingual is currently displayed on BenchLM for reference, but it is excluded from the weighted scoring formula.

## Leaderboard (2 models)

| Rank | Model | Creator | Score |
|------|-------|---------|-------|
| 1 | [LFM2.5-ColBERT-350M](/models/lfm2-5-colbert-350m) | LiquidAI | 60.5% |
| 2 | [LFM2.5-Embedding-350M](/models/lfm2-5-embedding-350m) | LiquidAI | 57.7% |

## FAQ

### What does NanoBEIR Multilingual measure?

A display-only multilingual retrieval benchmark reported by Liquid AI for LFM2.5 retriever models, using NDCG@10 across 11 languages.

### Which model scores highest on NanoBEIR Multilingual?

LFM2.5-ColBERT-350M by LiquidAI currently leads with a score of 60.5% on NanoBEIR Multilingual.

### How many models are evaluated on NanoBEIR Multilingual?

2 AI models have been evaluated on NanoBEIR Multilingual on BenchLM.

### Does NanoBEIR Multilingual affect BenchLM's overall score?

Not directly. NanoBEIR Multilingual is still displayed on BenchLM for reference, but it is excluded from the weighted scoring formula.

## Compare Top Models on NanoBEIR Multilingual

- [LFM2.5-ColBERT-350M vs LFM2.5-Embedding-350M](/compare/lfm2-5-colbert-350m-vs-lfm2-5-embedding-350m)
