**Qodo-Embed-1 is a state-of-the-art** code embedding model designed for retrieval tasks in the software development domain. It is offered in two sizes: lite (1.5B) and medium (7B). The model is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search, retrieval-augmented generation (RAG), and contextual understanding of programming languages. This model outperforms all previous open-source models in the COIR and MTEB leaderboards, achieving best-in-class performance with a significantly smaller size compared to competing models.
The **Qwen3-VL-Embedding** and **Qwen3-VL-Reranker** model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities. While the Embedding model generates high-dimensional vectors for broad applications like retrieval and clustering, the Reranker model is engineered to refine these results, establishing a comprehensive pipeline for state-of-the-art multimodal search. - **Multimodal Versatility**: Both models seamlessly handle a wide range of inputs—including text, images, screenshots, and video—within a unified framework. They deliver state-of-the-art performance across diverse multimodal tasks such as image-text retrieval, video-text matching, visual question answering (VQA), and multimodal content clustering.
**Qwen3Guard** is a series of safety moderation models built upon Qwen3 and trained on a dataset of 1.19 million prompts and responses labeled for safety. The series includes models of three sizes (0.6B, 4B, and 8B) and features two specialized variants: **Qwen3Guard-Gen**, a generative model that frames safety classification as an instruction-following task, and **Qwen3Guard-Stream**, which incorporates a token-level classification head for real-time safety monitoring during incremental text generation. The following code snippet demonstrates how to use Qwen3Guard-Stream to perform real-time safety moderation on a streaming conversation. > [!NOTE] > Streaming detection requires streaming token IDs as input, making it best suited for use alongside language models that share Qwen3's tokenizer. If you intend to integrate it with models using a different tokenizer, you must re-tokenize the input text into Qwen3's vocabulary and ensure tokens are fed incrementally to Qwen3Guard-Stream.
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COST EXPLORER
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Cost analysis
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1 M = 1 million tokens · USD Compares base text-token rates within the same channel
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MODEL HANDBOOK
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Selection guide & FAQ
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.
