intfloat
Intfloat: Multilingual-E5-Large
intfloat/multilingual-e5-large
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
intfloat/multilingual-e5-large is a multilingual text embedding model that, per the OpenRouter catalog, maps sentences, paragraphs, and documents across more than 90 languages into a 1024-dimensional dense vector space, with a 512-token context limit. The model documentation reports MTEB evaluations covering classification, retrieval, clustering, reranking, semantic similarity, and bitext mining, with strong measured results on cross-lingual mining tasks and more mixed outcomes on some classification and retrieval benchmarks.[1][2]
Strengths
- The catalog reports coverage of over 90 languages and 1024-dimensional embeddings positioned for multilingual retrieval and cross-language similarity.[1]
- Documented MTEB results show near-ceiling bitext mining accuracy on BUCC across German, French, Russian, and Chinese pairs with English, plus strong recall-oriented retrieval scores on FEVER and ArguAna.[2]
- The model card tags it for sentence-transformers, sentence-similarity, and feature-extraction workflows, indicating an established tooling ecosystem.[2]
Limitations
- Input is capped at a 512-token context, so longer documents require truncation or chunking.[1]
- Measured results are uneven: the model card reports comparatively low scores on multi-class Amazon reviews classification, emotion classification, and ClimateFEVER retrieval.[2]
- Evidence here is limited to a catalog entry and published benchmark listings; quality on a specific workload or domain should be validated before adoption.[1][2]
Where to use it
- Multilingual semantic search and cross-language retrieval, matching the catalog's stated optimization and the strong measured bitext mining results.[1][2]
- Sentence similarity and feature extraction pipelines built on sentence-transformers tooling.[2]
- Clustering or reranking of short-to-medium texts that fit within the 512-token context window.[1][2]
Sources
- [1] OpenRouter catalog · Intfloat: Multilingual-E5-LargeSources checked: Nov 13, 2026
- [2] Model documentation · Intfloat: Multilingual-E5-LargeSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and...
Specifications
- Model ID
- intfloat/multilingual-e5-large
- Canonical ID
- intfloat/multilingual-e5-large-20251117
- Creator
- intfloat
- Added to OpenRouter
- 18.11.2025
- Context length
- 512
- Modality
- text->embeddings
- Input modalities
- text
- Output modalities
- embeddings
- Token pricing
- USD per 1M tokens: in $0.010 / out $0
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | — |
| Coding | No measurement | — |
| Agents | No measurement | — |
| Writing | No measurement | — |
| Reasoning | No measurement | — |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | output: embeddings |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| DeepInfrafp32 | 512 | $0.010 | $0 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.