Mixedbread

Introducing Mixedbread Wholembed v3, our new SOTA retrieval model across all modalities and 100+ languages. Wholembed v3 brings best-in-class search to text, audio, images, PDFs, videos... You can now get the best retrieval performance on your data, no matter its format.

I've been eagerly awaiting this release from the @mixedbreadai folks. They're world-leading experts in late interaction retrieval. And today they remind us that late interaction done well makes all your favorite embedding models look like they don't work.

Find out more about the model and its performance here: mixedbread.com/blog/wholembed…

Wholembed v3 is available immediately through Mixedbread Search. You can try it on our platform now, for free: New users get 2M free tokens to get started. Startups can receive much more through our partnered accelerator programs with Vercel and TinyFish. mixedbread.com

Introducing Mixedbread Wholembed v3, our new SOTA retrieval model across all modalities and 100+ languages. Wholembed v3 brings best-in-class search to text, audio, images, PDFs, videos... You can now get the best retrieval performance on your data, no matter its format.

The SIGIR deadline has now passed. You know what hasn't passed? The Late Interaction Workshop deadline! Which we are extending to February 20th AOE. We care about everything around multi-vector retrieval: indexing, field reports, model training, multimodal, you name it.

We're building Mixedbread to close the gap between Search that is possible today and what the users of tomorrow will demand. You can read more about it here: mixedbread.com/blog/multimoda…

Retrieval has always been, and continues to be the natural interface to information. Traditionally, it was how you found useful websites and relevant snippets. Nowadays, it's how agents find exactly the pieces of context they need to answer a user's query. But retrieval has been stagnant for too long, and it has become clear that the once-omnipresent single-vector text representation is not meeting the needs of this new generation of users. Agents need the model to be able to understand long, reasoning-intensive queries. They need the ability to retrieve documents where they live, be they text, images, pdfs or even videos.

We build the first production ready multi-vector and multimodal search. Now we are serving over 1 billion documents in under 50ms latency (p50). We are sharing how we build it.

mgrep is now available for opencode. It makes it 4x faster and uses 3x less tokens. simply run mgrep install-opencode

I got better quality, half the tokens, and twice the speed with @mixedbreadai 's mgrep The "trick" is a multi-vector architecture Why only now? Because the arch and quantization details are hard Join @aaxsh18 for a research talk on how it works 👇 maven.com/p/0c0eed/moder…

opus 4.5 is impressive. anthropic forgot a bar in their report. nano banana just added it for me. the leaderboard: huggingface.co/spaces/Tevatro…