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Harrier is an open-source embedding model from the Bing search team that ranked…

Brief

Harrier is an open-source embedding model from the Bing search team that ranked #1 on the multilingual MTEB-v2 benchmark (announced 2026-04-07). It supports 100+ languages, handles inputs up to 32K, and targets retrieval, semantic matching, and web-grounding/RAG for Bing and AI chatbots, outperforming Gemini-, Gemma-, Llama-, and Qwen-based models.

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title: @mustafasuleyman: Another SOTA model drop! This time from the @Bing team: meet Harrier, a new open-source embedding mo...

author: @mustafasuleyman

content_type: tweet

publication: Twitter/X

published: 2026-04-07T16:22:39+00:00

source_url: https://x.com/mustafasuleyman/status/2041552243019980929

word_count: 185

Another SOTA model drop! This time from the @Bing team: meet Harrier, a new open-source embedding model with state-of-the-art performance and the #1 spot on the industry standard multilingual MTEB-v2 benchmark.

Jordi Ribas (@JordiRib1)

I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard.

Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today.

As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone.

Learn more: msft.it/6019QNB0b

— https://nitter.net/JordiRib1/status/2041550352739164404#m