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BGE-M3 is BAAI's open-weight multilingual text embedding model for dense, sparse, and multi-vector retrieval. It covers 100+ languages with an 8,192-token input window and returns a 1,024-dimension dense vector.

BGE Multilingual Gemma 2 is BAAI's open-weight compact multilingual text embedding model built on Gemma 2. It is trained across 100+ languages, takes an 8,192-token input window, and returns a 3,584-dimension dense vector.

Llama 3.3 70B Instruct is Meta's open-weight instruction-tuned model with 70B parameters and a 131072-token context window. It is tuned for multilingual dialogue, with tool use on par with Llama 3.1 70B. It suits chat, reasoning, and coding.

Mistral Small 3.2 24B Instruct is an open-weight 24B instruction model under Apache 2.0, with a 131k context window. It improves instruction following and function calling over Mistral Small 3.1 and takes text and image input.

GPT-OSS-120b is OpenAI's open-weight mixture-of-experts reasoning model under the Apache-2.0 license. It has 116.8B total and 5.1B active parameters and a 131,072-token context window. It supports function calling and runs on one 80 GB GPU with MXFP4 weights.

GPT-OSS 20B is OpenAI's open-weight reasoning model for agentic tasks and low-latency edge use, under Apache-2.0. It is a mixture-of-experts model with 20.9B total and 3.6B active parameters and a 131,072-token context. It runs within 16 GB of memory with MXFP4 weights.

Qwen3 Embedding 8B is Qwen's open-weight 8B text embedding model with a 32,768-token context window. It takes text in and returns embedding vectors.

Qwen 3.5 397B A17B is Alibaba's open-weight mixture-of-experts model with 397B total and 17B active parameters. It supports reasoning and function calling, and takes text, image and video input.

Qwen3.5-9B is an open-weight model from Alibaba's Qwen3.5 series with a hybrid Gated Delta Networks and sparse MoE architecture. It has a thinking mode, tool calling and 201 languages.

Qwen3.8-27B is Alibaba's open-weight dense Qwen3.8 model for agentic coding and complex reasoning, with a native thinking mode. It targets multi-step agent work and large coding tasks.