The best performing model on SWE-Bench Pro is open-source on @huggingface! Welcome GLM 5.1! huggingface.co/zai-org/GLM-5…
→ View original post on X — @clementdelangue, 2026-04-07 16:31 UTC
RESEARCH
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GLM-5.1: Revolutionary Open-Source Agentic Coding Model Released
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Another big release: GLM-5.1! China is on fire! significant increase in evals compared to GLM-5.0 tl;dr GLM-5.1 is the new open-source agentic coding model that significantly outperforms its predecessor by sustaining long-horizon problem-solving over hundreds of iterations, continuously improving results instead of plateauing, achieving state-of-the-art performance on complex software engineering benchmarks. Z.ai (@Zai_org) Introducing GLM-5.1: The Next Level of Open Source – Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. – Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: z.ai/blog/glm-5.1 Weights: huggingface.co/zai-org/GLM-5… API: docs.z.ai/guides/llm/glm-5.1 Coding Plan: z.ai/subscribe Coming to chat.z.ai in the next few days. — https://nitter.net/Zai_org/status/2041550153354519022#m
→ View original post on X — @kimmonismus, 2026-04-07 16:27 UTC
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Embeddings: The Unsung Hero Driving Model Accuracy Forward
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Embedding is an unsung hero in model accuracy and today is a big leap forward. It's at the heart of grounding; it's the layer that does the hard work of searching, retrieving, organizing, and connecting information across sources for a holistic response.
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Harrier Upgrades Bing Web Grounding for Agentic AI Era
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Bing's web grounding already powers almost every major AI chatbot today. With Harrier, it just got a big upgrade for the agentic era. Better embeddings lead to better retrieval, often more accurate answers, and better multilingual performance in the 100+ languages Harrier
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Microsoft Open-Sources Industry-Leading Embedding Model Harrier
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Big kudos to @JordiRib1 and the @bing team – awesome to see the speed and quality shipping across @MicrosoftAI
. More on Harrier in today's blog: https://
blogs.bing.com/search/April-2
026/Microsoft-Open-Sources-Industry-Leading-Embedding-Model
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Bing releases Harrier, new state-of-the-art embedding model
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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
→ View original post on X — @clementdelangue, 2026-04-07 16:22 UTC
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GLM-5.1: Open-Source AI Tops Coding Benchmarks
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BREAKING : Z AI released GLM-5.1, an open-source model with top tier coding performance! “Number 1 in open source and number 3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo.” “Runs autonomously for 8 hours, refining strategies through thousands of iterations.”
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Microsoft Open Sources Harrier: Top Multilingual Embedding Model
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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.
— Jordi Ribas (@JordiRib1) 7 avril 2026
Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across… pic.twitter.com/asMVydbijhI’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
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OpenMed dataset added to Hugging Face platform
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just added openmed data on @huggingface, what else
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GLM 5.1, DeepSeek v4, and Minimax 2.7 Launch Announcement
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BIG DAY: GLM 5.1, DeepSeek v4, Minimax 2.7 is COMING