@lobehub/chat
Version:
Lobe Chat - an open-source, high-performance chatbot framework that supports speech synthesis, multimodal, and extensible Function Call plugin system. Supports one-click free deployment of your private ChatGPT/LLM web application.
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{
"01-ai/yi-1.5-34b-chat": {
"description": "Zero One Everything, the latest open-source fine-tuned model with 34 billion parameters, supports various dialogue scenarios with high-quality training data aligned with human preferences."
},
"01-ai/yi-1.5-9b-chat": {
"description": "Zero One Everything, the latest open-source fine-tuned model with 9 billion parameters, supports various dialogue scenarios with high-quality training data aligned with human preferences."
},
"360/deepseek-r1": {
"description": "[360 Deployment Version] DeepSeek-R1 extensively utilizes reinforcement learning techniques in the post-training phase, significantly enhancing model inference capabilities with minimal labeled data. It performs comparably to OpenAI's o1 official version in tasks such as mathematics, coding, and natural language reasoning."
},
"360gpt-pro": {
"description": "360GPT Pro, as an important member of the 360 AI model series, meets diverse natural language application scenarios with efficient text processing capabilities, supporting long text understanding and multi-turn dialogue."
},
"360gpt-pro-trans": {
"description": "A translation-specific model, finely tuned for optimal translation results."
},
"360gpt-turbo": {
"description": "360GPT Turbo offers powerful computation and dialogue capabilities, with excellent semantic understanding and generation efficiency, making it an ideal intelligent assistant solution for enterprises and developers."
},
"360gpt-turbo-responsibility-8k": {
"description": "360GPT Turbo Responsibility 8K emphasizes semantic safety and responsibility, designed specifically for applications with high content safety requirements, ensuring accuracy and robustness in user experience."
},
"360gpt2-o1": {
"description": "360gpt2-o1 builds a chain of thought using tree search and incorporates a reflection mechanism, trained with reinforcement learning, enabling the model to self-reflect and correct errors."
},
"360gpt2-pro": {
"description": "360GPT2 Pro is an advanced natural language processing model launched by 360, featuring exceptional text generation and understanding capabilities, particularly excelling in generation and creative tasks, capable of handling complex language transformations and role-playing tasks."
},
"360zhinao2-o1": {
"description": "360zhinao2-o1 uses tree search to build a chain of thought and introduces a reflection mechanism, utilizing reinforcement learning for training, enabling the model to possess self-reflection and error-correction capabilities."
},
"4.0Ultra": {
"description": "Spark4.0 Ultra is the most powerful version in the Spark large model series, enhancing text content understanding and summarization capabilities while upgrading online search links. It is a comprehensive solution for improving office productivity and accurately responding to demands, leading the industry as an intelligent product."
},
"AnimeSharp": {
"description": "AnimeSharp (also known as “4x-AnimeSharp”) is an open-source super-resolution model developed by Kim2091 based on the ESRGAN architecture, focusing on upscaling and sharpening anime-style images. It was renamed from “4x-TextSharpV1” in February 2022, originally also suitable for text images but significantly optimized for anime content."
},
"Baichuan2-Turbo": {
"description": "Utilizes search enhancement technology to achieve comprehensive links between large models and domain knowledge, as well as knowledge from the entire web. Supports uploads of various documents such as PDF and Word, and URL input, providing timely and comprehensive information retrieval with accurate and professional output."
},
"Baichuan3-Turbo": {
"description": "Optimized for high-frequency enterprise scenarios, significantly improving performance and cost-effectiveness. Compared to the Baichuan2 model, content creation improves by 20%, knowledge Q&A by 17%, and role-playing ability by 40%. Overall performance is superior to GPT-3.5."
},
"Baichuan3-Turbo-128k": {
"description": "Features a 128K ultra-long context window, optimized for high-frequency enterprise scenarios, significantly improving performance and cost-effectiveness. Compared to the Baichuan2 model, content creation improves by 20%, knowledge Q&A by 17%, and role-playing ability by 40%. Overall performance is superior to GPT-3.5."
},
"Baichuan4": {
"description": "The model is the best in the country, surpassing mainstream foreign models in Chinese tasks such as knowledge encyclopedias, long texts, and creative generation. It also boasts industry-leading multimodal capabilities, excelling in multiple authoritative evaluation benchmarks."
},
"Baichuan4-Air": {
"description": "The leading model in the country, surpassing mainstream foreign models in Chinese tasks such as knowledge encyclopedias, long texts, and creative generation. It also possesses industry-leading multimodal capabilities, excelling in multiple authoritative evaluation benchmarks."
},
"Baichuan4-Turbo": {
"description": "The leading model in the country, surpassing mainstream foreign models in Chinese tasks such as knowledge encyclopedias, long texts, and creative generation. It also possesses industry-leading multimodal capabilities, excelling in multiple authoritative evaluation benchmarks."
},
"DeepSeek-R1": {
"description": "A state-of-the-art efficient LLM, skilled in reasoning, mathematics, and programming."
},
"DeepSeek-R1-Distill-Llama-70B": {
"description": "DeepSeek R1— the larger and smarter model in the DeepSeek suite— distilled into the Llama 70B architecture. Based on benchmark testing and human evaluation, this model is smarter than the original Llama 70B, particularly excelling in tasks requiring mathematical and factual accuracy."
},
"DeepSeek-R1-Distill-Qwen-1.5B": {
"description": "The DeepSeek-R1 distillation model based on Qwen2.5-Math-1.5B optimizes inference performance through reinforcement learning and cold-start data, refreshing the benchmark for open-source models across multiple tasks."
},
"DeepSeek-R1-Distill-Qwen-14B": {
"description": "The DeepSeek-R1 distillation model based on Qwen2.5-14B optimizes inference performance through reinforcement learning and cold-start data, refreshing the benchmark for open-source models across multiple tasks."
},
"DeepSeek-R1-Distill-Qwen-32B": {
"description": "The DeepSeek-R1 series optimizes inference performance through reinforcement learning and cold-start data, refreshing the benchmark for open-source models across multiple tasks, surpassing the level of OpenAI-o1-mini."
},
"DeepSeek-R1-Distill-Qwen-7B": {
"description": "The DeepSeek-R1 distillation model based on Qwen2.5-Math-7B optimizes inference performance through reinforcement learning and cold-start data, refreshing the benchmark for open-source models across multiple tasks."
},
"DeepSeek-V3": {
"description": "DeepSeek-V3 is a MoE model developed in-house by Deep Seek Company. Its performance surpasses that of other open-source models such as Qwen2.5-72B and Llama-3.1-405B in multiple assessments, and it stands on par with the world's top proprietary models like GPT-4o and Claude-3.5-Sonnet."
},
"Doubao-lite-128k": {
"description": "Doubao-lite offers ultra-fast response times and better cost-effectiveness, providing customers with more flexible options for different scenarios. Supports inference and fine-tuning with a 128k context window."
},
"Doubao-lite-32k": {
"description": "Doubao-lite offers ultra-fast response times and better cost-effectiveness, providing customers with more flexible options for different scenarios. Supports inference and fine-tuning with a 32k context window."
},
"Doubao-lite-4k": {
"description": "Doubao-lite offers ultra-fast response times and better cost-effectiveness, providing customers with more flexible options for different scenarios. Supports inference and fine-tuning with a 4k context window."
},
"Doubao-pro-128k": {
"description": "The best-performing flagship model, suitable for handling complex tasks. It excels in scenarios such as reference Q&A, summarization, creative writing, text classification, and role-playing. Supports inference and fine-tuning with a 128k context window."
},
"Doubao-pro-32k": {
"description": "The best-performing flagship model, suitable for handling complex tasks. It excels in scenarios such as reference Q&A, summarization, creative writing, text classification, and role-playing. Supports inference and fine-tuning with a 32k context window."
},
"Doubao-pro-4k": {
"description": "The best-performing flagship model, suitable for handling complex tasks. It excels in scenarios such as reference Q&A, summarization, creative writing, text classification, and role-playing. Supports inference and fine-tuning with a 4k context window."
},
"DreamO": {
"description": "DreamO is an open-source image customization generation model jointly developed by ByteDance and Peking University, designed to support multi-task image generation through a unified architecture. It employs an efficient compositional modeling approach to generate highly consistent and customized images based on multiple user-specified conditions such as identity, subject, style, and background."
},
"ERNIE-3.5-128K": {
"description": "Baidu's self-developed flagship large-scale language model, covering a vast amount of Chinese and English corpus. It possesses strong general capabilities, meeting the requirements for most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu's search plugin to ensure the timeliness of Q&A information."
},
"ERNIE-3.5-8K": {
"description": "Baidu's self-developed flagship large-scale language model, covering a vast amount of Chinese and English corpus. It possesses strong general capabilities, meeting the requirements for most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu's search plugin to ensure the timeliness of Q&A information."
},
"ERNIE-3.5-8K-Preview": {
"description": "Baidu's self-developed flagship large-scale language model, covering a vast amount of Chinese and English corpus. It possesses strong general capabilities, meeting the requirements for most dialogue Q&A, creative generation, and plugin application scenarios; it supports automatic integration with Baidu's search plugin to ensure the timeliness of Q&A information."
},
"ERNIE-4.0-8K-Latest": {
"description": "Baidu's self-developed flagship ultra-large-scale language model, which has achieved a comprehensive upgrade in model capabilities compared to ERNIE 3.5, widely applicable to complex task scenarios across various fields; supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
},
"ERNIE-4.0-8K-Preview": {
"description": "Baidu's self-developed flagship ultra-large-scale language model, which has achieved a comprehensive upgrade in model capabilities compared to ERNIE 3.5, widely applicable to complex task scenarios across various fields; supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information."
},
"ERNIE-4.0-Turbo-8K-Latest": {
"description": "Baidu's self-developed flagship ultra-large-scale language model, demonstrating excellent overall performance, suitable for complex task scenarios across various fields; supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information. It offers better performance compared to ERNIE 4.0."
},
"ERNIE-4.0-Turbo-8K-Preview": {
"description": "Baidu's self-developed flagship ultra-large-scale language model, demonstrating excellent overall performance, widely applicable to complex task scenarios across various fields; supports automatic integration with Baidu search plugins to ensure the timeliness of Q&A information. It outperforms ERNIE 4.0 in performance."
},
"ERNIE-Character-8K": {
"description": "Baidu's self-developed vertical scene large language model, suitable for applications such as game NPCs, customer service dialogues, and role-playing conversations, featuring more distinct and consistent character styles, stronger adherence to instructions, and superior inference performance."
},
"ERNIE-Lite-Pro-128K": {
"description": "Baidu's self-developed lightweight large language model, balancing excellent model performance with inference efficiency, offering better results than ERNIE Lite, suitable for inference on low-power AI acceleration cards."
},
"ERNIE-Speed-128K": {
"description": "Baidu's latest self-developed high-performance large language model released in 2024, with outstanding general capabilities, suitable as a base model for fine-tuning, effectively addressing specific scenario issues while also exhibiting excellent inference performance."
},
"ERNIE-Speed-Pro-128K": {
"description": "Baidu's latest self-developed high-performance large language model released in 2024, with outstanding general capabilities, providing better results than ERNIE Speed, suitable as a base model for fine-tuning, effectively addressing specific scenario issues while also exhibiting excellent inference performance."
},
"FLUX.1-Kontext-dev": {
"description": "FLUX.1-Kontext-dev is a multimodal image generation and editing model developed by Black Forest Labs based on the Rectified Flow Transformer architecture, featuring 12 billion parameters. It specializes in generating, reconstructing, enhancing, or editing images under given contextual conditions. The model combines the controllable generation advantages of diffusion models with the contextual modeling capabilities of Transformers, supporting high-quality image output and widely applicable to image restoration, completion, and visual scene reconstruction tasks."
},
"FLUX.1-dev": {
"description": "FLUX.1-dev is an open-source multimodal language model (MLLM) developed by Black Forest Labs, optimized for vision-and-language tasks by integrating image and text understanding and generation capabilities. Built upon advanced large language models such as Mistral-7B, it achieves vision-language collaborative processing and complex task reasoning through a carefully designed visual encoder and multi-stage instruction fine-tuning."
},
"Gryphe/MythoMax-L2-13b": {
"description": "MythoMax-L2 (13B) is an innovative model suitable for multi-domain applications and complex tasks."
},
"HelloMeme": {
"description": "HelloMeme is an AI tool that automatically generates memes, GIFs, or short videos based on the images or actions you provide. It requires no drawing or programming skills; simply prepare reference images, and it will help you create visually appealing, fun, and stylistically consistent content."
},
"HiDream-I1-Full": {
"description": "HiDream-E1-Full is an open-source multimodal image editing large model launched by HiDream.ai, based on the advanced Diffusion Transformer architecture combined with powerful language understanding capabilities (embedded LLaMA 3.1-8B-Instruct). It supports image generation, style transfer, local editing, and content repainting through natural language instructions, demonstrating excellent vision-language comprehension and execution abilities."
},
"HunyuanDiT-v1.2-Diffusers-Distilled": {
"description": "hunyuandit-v1.2-distilled is a lightweight text-to-image model optimized through distillation, capable of rapidly generating high-quality images, especially suitable for low-resource environments and real-time generation tasks."
},
"InstantCharacter": {
"description": "InstantCharacter is a tuning-free personalized character generation model released by Tencent AI team in 2025, designed to achieve high-fidelity, cross-scene consistent character generation. The model supports character modeling based on a single reference image and can flexibly transfer the character to various styles, actions, and backgrounds."
},
"InternVL2-8B": {
"description": "InternVL2-8B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers."
},
"InternVL2.5-26B": {
"description": "InternVL2.5-26B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers."
},
"Kolors": {
"description": "Kolors is a text-to-image model developed by the Kuaishou Kolors team. Trained with billions of parameters, it excels in visual quality, Chinese semantic understanding, and text rendering."
},
"Kwai-Kolors/Kolors": {
"description": "Kolors is a large-scale latent diffusion text-to-image generation model developed by the Kuaishou Kolors team. Trained on billions of text-image pairs, it demonstrates significant advantages in visual quality, complex semantic accuracy, and Chinese and English character rendering. It supports both Chinese and English inputs and performs exceptionally well in understanding and generating Chinese-specific content."
},
"Llama-3.2-11B-Vision-Instruct": {
"description": "Exhibits outstanding image reasoning capabilities on high-resolution images, suitable for visual understanding applications."
},
"Llama-3.2-90B-Vision-Instruct\t": {
"description": "Advanced image reasoning capabilities suitable for visual understanding agent applications."
},
"Meta-Llama-3.1-405B-Instruct": {
"description": "Llama 3.1 instruction-tuned text model optimized for multilingual dialogue use cases, performing excellently on common industry benchmarks among many available open-source and closed chat models."
},
"Meta-Llama-3.1-70B-Instruct": {
"description": "Llama 3.1 instruction-tuned text model optimized for multilingual dialogue use cases, performing excellently on common industry benchmarks among many available open-source and closed chat models."
},
"Meta-Llama-3.1-8B-Instruct": {
"description": "Llama 3.1 instruction-tuned text model optimized for multilingual dialogue use cases, performing excellently on common industry benchmarks among many available open-source and closed chat models."
},
"Meta-Llama-3.2-1B-Instruct": {
"description": "An advanced cutting-edge small language model with language understanding, excellent reasoning capabilities, and text generation abilities."
},
"Meta-Llama-3.2-3B-Instruct": {
"description": "An advanced cutting-edge small language model with language understanding, excellent reasoning capabilities, and text generation abilities."
},
"Meta-Llama-3.3-70B-Instruct": {
"description": "Llama 3.3 is the most advanced multilingual open-source large language model in the Llama series, offering performance comparable to a 405B model at a very low cost. Based on the Transformer architecture, it enhances usability and safety through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Its instruction-tuned version is optimized for multilingual dialogue and outperforms many open-source and closed chat models on various industry benchmarks. Knowledge cutoff date is December 2023."
},
"MiniMax-M1": {
"description": "A brand-new self-developed inference model. Globally leading: 80K reasoning chains x 1M input, performance comparable to top overseas models."
},
"MiniMax-Text-01": {
"description": "In the MiniMax-01 series of models, we have made bold innovations: for the first time, we have implemented a linear attention mechanism on a large scale, making the traditional Transformer architecture no longer the only option. This model has a parameter count of up to 456 billion, with a single activation of 45.9 billion. Its overall performance rivals that of top overseas models while efficiently handling the world's longest context of 4 million tokens, which is 32 times that of GPT-4o and 20 times that of Claude-3.5-Sonnet."
},
"MiniMaxAI/MiniMax-M1-80k": {
"description": "MiniMax-M1 is a large-scale hybrid attention inference model with open-source weights, featuring 456 billion parameters, with approximately 45.9 billion parameters activated per token. The model natively supports ultra-long contexts of up to 1 million tokens and, through lightning attention mechanisms, reduces floating-point operations by 75% compared to DeepSeek R1 in tasks generating 100,000 tokens. Additionally, MiniMax-M1 employs a Mixture of Experts (MoE) architecture, combining the CISPO algorithm with an efficient reinforcement learning training design based on hybrid attention, achieving industry-leading performance in long-input inference and real-world software engineering scenarios."
},
"Moonshot-Kimi-K2-Instruct": {
"description": "With a total of 1 trillion parameters and 32 billion activated parameters, this non-thinking model achieves top-tier performance in cutting-edge knowledge, mathematics, and coding, excelling in general agent tasks. It is carefully optimized for agent tasks, capable not only of answering questions but also taking actions. Ideal for improvisational, general chat, and agent experiences, it is a reflex-level model requiring no prolonged thinking."
},
"NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO": {
"description": "Nous Hermes 2 - Mixtral 8x7B-DPO (46.7B) is a high-precision instruction model suitable for complex computations."
},
"OmniConsistency": {
"description": "OmniConsistency enhances style consistency and generalization in image-to-image tasks by introducing large-scale Diffusion Transformers (DiTs) and paired stylized data, effectively preventing style degradation."
},
"Phi-3-medium-128k-instruct": {
"description": "The same Phi-3-medium model, but with a larger context size for RAG or few-shot prompting."
},
"Phi-3-medium-4k-instruct": {
"description": "A 14B parameter model that provides better quality than Phi-3-mini, focusing on high-quality, reasoning-dense data."
},
"Phi-3-mini-128k-instruct": {
"description": "The same Phi-3-mini model, but with a larger context size for RAG or few-shot prompting."
},
"Phi-3-mini-4k-instruct": {
"description": "The smallest member of the Phi-3 family, optimized for both quality and low latency."
},
"Phi-3-small-128k-instruct": {
"description": "The same Phi-3-small model, but with a larger context size for RAG or few-shot prompting."
},
"Phi-3-small-8k-instruct": {
"description": "A 7B parameter model that provides better quality than Phi-3-mini, focusing on high-quality, reasoning-dense data."
},
"Phi-3.5-mini-instruct": {
"description": "An updated version of the Phi-3-mini model."
},
"Phi-3.5-vision-instrust": {
"description": "An updated version of the Phi-3-vision model."
},
"Pro/Qwen/Qwen2-7B-Instruct": {
"description": "Qwen2-7B-Instruct is an instruction-tuned large language model in the Qwen2 series, with a parameter size of 7B. This model is based on the Transformer architecture and employs techniques such as the SwiGLU activation function, attention QKV bias, and group query attention. It can handle large-scale inputs. The model excels in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning across multiple benchmark tests, surpassing most open-source models and demonstrating competitive performance comparable to proprietary models in certain tasks. Qwen2-7B-Instruct outperforms Qwen1.5-7B-Chat in multiple evaluations, showing significant performance improvements."
},
"Pro/Qwen/Qwen2.5-7B-Instruct": {
"description": "Qwen2.5-7B-Instruct is one of the latest large language models released by Alibaba Cloud. This 7B model shows significant improvements in coding and mathematics. It also provides multilingual support, covering over 29 languages, including Chinese and English. The model has made notable advancements in instruction following, understanding structured data, and generating structured outputs, especially JSON."
},
"Pro/Qwen/Qwen2.5-Coder-7B-Instruct": {
"description": "Qwen2.5-Coder-7B-Instruct is the latest version in Alibaba Cloud's series of code-specific large language models. This model significantly enhances code generation, reasoning, and repair capabilities based on Qwen2.5, trained on 55 trillion tokens. It not only improves coding abilities but also maintains advantages in mathematics and general capabilities, providing a more comprehensive foundation for practical applications such as code agents."
},
"Pro/Qwen/Qwen2.5-VL-7B-Instruct": {
"description": "Qwen2.5-VL is the newest addition to the Qwen series, featuring enhanced visual comprehension capabilities. It can analyze text, charts, and layouts within images, comprehend long videos while capturing events. The model supports reasoning, tool manipulation, multi-format object localization, and structured output generation. It incorporates optimized dynamic resolution and frame rate training for video understanding, along with improved efficiency in its visual encoder."
},
"Pro/THUDM/GLM-4.1V-9B-Thinking": {
"description": "GLM-4.1V-9B-Thinking is an open-source vision-language model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG Lab, designed specifically for handling complex multimodal cognitive tasks. Based on the GLM-4-9B-0414 foundation model, it significantly enhances cross-modal reasoning ability and stability by introducing the Chain-of-Thought reasoning mechanism and employing reinforcement learning strategies."
},
"Pro/THUDM/glm-4-9b-chat": {
"description": "GLM-4-9B-Chat is the open-source version of the GLM-4 series pre-trained models launched by Zhipu AI. This model excels in semantics, mathematics, reasoning, code, and knowledge. In addition to supporting multi-turn dialogues, GLM-4-9B-Chat also features advanced capabilities such as web browsing, code execution, custom tool invocation (Function Call), and long-text reasoning. The model supports 26 languages, including Chinese, English, Japanese, Korean, and German. In multiple benchmark tests, GLM-4-9B-Chat has demonstrated excellent performance, such as in AlignBench-v2, MT-Bench, MMLU, and C-Eval. The model supports a maximum context length of 128K, making it suitable for academic research and commercial applications."
},
"Pro/deepseek-ai/DeepSeek-R1": {
"description": "DeepSeek-R1 is a reinforcement learning (RL) driven inference model that addresses issues of repetitiveness and readability in models. Prior to RL, DeepSeek-R1 introduced cold start data to further optimize inference performance. It performs comparably to OpenAI-o1 in mathematical, coding, and reasoning tasks, and enhances overall effectiveness through carefully designed training methods."
},
"Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B": {
"description": "DeepSeek-R1-Distill-Qwen-7B is a model derived from Qwen2.5-Math-7B through knowledge distillation. It was fine-tuned using 800,000 carefully selected samples generated by DeepSeek-R1, demonstrating exceptional reasoning capabilities. The model achieves outstanding performance across multiple benchmarks, including 92.8% accuracy on MATH-500, a 55.5% pass rate on AIME 2024, and a score of 1189 on CodeForces, showcasing strong mathematical and programming abilities for a 7B-scale model."
},
"Pro/deepseek-ai/DeepSeek-V3": {
"description": "DeepSeek-V3 is a mixed expert (MoE) language model with 671 billion parameters, utilizing multi-head latent attention (MLA) and the DeepSeekMoE architecture, combined with a load balancing strategy without auxiliary loss to optimize inference and training efficiency. Pre-trained on 14.8 trillion high-quality tokens and fine-tuned with supervision and reinforcement learning, DeepSeek-V3 outperforms other open-source models and approaches leading closed-source models."
},
"Pro/moonshotai/Kimi-K2-Instruct": {
"description": "Kimi K2 is a MoE architecture base model with exceptional coding and agent capabilities, featuring 1 trillion total parameters and 32 billion activated parameters. In benchmark tests across general knowledge reasoning, programming, mathematics, and agent tasks, the K2 model outperforms other mainstream open-source models."
},
"QwQ-32B-Preview": {
"description": "QwQ-32B-Preview is an innovative natural language processing model capable of efficiently handling complex dialogue generation and context understanding tasks."
},
"Qwen/QVQ-72B-Preview": {
"description": "QVQ-72B-Preview is a research-oriented model developed by the Qwen team, focusing on visual reasoning capabilities, with unique advantages in understanding complex scenes and solving visually related mathematical problems."
},
"Qwen/QwQ-32B": {
"description": "QwQ is the inference model of the Qwen series. Compared to traditional instruction-tuned models, QwQ possesses reasoning and cognitive abilities, achieving significantly enhanced performance in downstream tasks, especially in solving difficult problems. QwQ-32B is a medium-sized inference model that competes effectively against state-of-the-art inference models (such as DeepSeek-R1 and o1-mini). This model employs technologies such as RoPE, SwiGLU, RMSNorm, and Attention QKV bias, featuring a 64-layer network structure and 40 Q attention heads (with 8 KV heads in the GQA architecture)."
},
"Qwen/QwQ-32B-Preview": {
"description": "QwQ-32B-Preview is Qwen's latest experimental research model, focusing on enhancing AI reasoning capabilities. By exploring complex mechanisms such as language mixing and recursive reasoning, its main advantages include strong analytical reasoning, mathematical, and programming abilities. However, it also faces challenges such as language switching issues, reasoning loops, safety considerations, and differences in other capabilities."
},
"Qwen/Qwen2-72B-Instruct": {
"description": "Qwen2 is an advanced general-purpose language model that supports various types of instructions."
},
"Qwen/Qwen2-7B-Instruct": {
"description": "Qwen2-72B-Instruct is an instruction-tuned large language model in the Qwen2 series, with a parameter size of 72B. This model is based on the Transformer architecture and employs techniques such as the SwiGLU activation function, attention QKV bias, and group query attention. It can handle large-scale inputs. The model excels in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning across multiple benchmark tests, surpassing most open-source models and demonstrating competitive performance comparable to proprietary models in certain tasks."
},
"Qwen/Qwen2-VL-72B-Instruct": {
"description": "Qwen2-VL is the latest iteration of the Qwen-VL model, achieving state-of-the-art performance in visual understanding benchmarks."
},
"Qwen/Qwen2.5-14B-Instruct": {
"description": "Qwen2.5 is a brand new series of large language models designed to optimize the handling of instruction-based tasks."
},
"Qwen/Qwen2.5-32B-Instruct": {
"description": "Qwen2.5 is a brand new series of large language models designed to optimize the handling of instruction-based tasks."
},
"Qwen/Qwen2.5-72B-Instruct": {
"description": "A large language model developed by the Alibaba Cloud Tongyi Qianwen team"
},
"Qwen/Qwen2.5-72B-Instruct-128K": {
"description": "Qwen2.5 is a new large language model series with enhanced understanding and generation capabilities."
},
"Qwen/Qwen2.5-72B-Instruct-Turbo": {
"description": "Qwen2.5 is a new large language model series designed to optimize instruction-based task processing."
},
"Qwen/Qwen2.5-7B-Instruct": {
"description": "Qwen2.5 is a brand new series of large language models designed to optimize the handling of instruction-based tasks."
},
"Qwen/Qwen2.5-7B-Instruct-Turbo": {
"description": "Qwen2.5 is a new large language model series designed to optimize instruction-based task processing."
},
"Qwen/Qwen2.5-Coder-32B-Instruct": {
"description": "Qwen2.5-Coder focuses on code writing."
},
"Qwen/Qwen2.5-Coder-7B-Instruct": {
"description": "Qwen2.5-Coder-7B-Instruct is the latest version in Alibaba Cloud's series of code-specific large language models. This model significantly enhances code generation, reasoning, and repair capabilities based on Qwen2.5, trained on 55 trillion tokens. It not only improves coding abilities but also maintains advantages in mathematics and general capabilities, providing a more comprehensive foundation for practical applications such as code agents."
},
"Qwen/Qwen2.5-VL-32B-Instruct": {
"description": "Qwen2.5-VL-32B-Instruct is a multimodal large language model developed by the Tongyi Qianwen team, representing part of the Qwen2.5-VL series. This model excels not only in recognizing common objects but also in analyzing text, charts, icons, graphics, and layouts within images. It functions as a visual agent capable of reasoning and dynamically manipulating tools, with the ability to operate computers and mobile devices. Additionally, the model can precisely locate objects in images and generate structured outputs for documents like invoices and tables. Compared to its predecessor Qwen2-VL, this version demonstrates enhanced mathematical and problem-solving capabilities through reinforcement learning, while also exhibiting more human-preferred response styles."
},
"Qwen/Qwen2.5-VL-72B-Instruct": {
"description": "Qwen2.5-VL is the vision-language model in the Qwen2.5 series. This model demonstrates significant improvements across multiple dimensions: enhanced visual comprehension capable of recognizing common objects, analyzing text, charts, and layouts; serving as a visual agent that can reason and dynamically guide tool usage; supporting understanding of long videos exceeding 1 hour while capturing key events; able to precisely locate objects in images by generating bounding boxes or points; and capable of producing structured outputs particularly suitable for scanned data like invoices and forms."
},
"Qwen/Qwen3-14B": {
"description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functions, and multilingual support, with a switchable thinking mode."
},
"Qwen/Qwen3-235B-A22B": {
"description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functions, and multilingual support, with a switchable thinking mode."
},
"Qwen/Qwen3-235B-A22B-Instruct-2507": {
"description": "Qwen3-235B-A22B-Instruct-2507 is a flagship mixture-of-experts (MoE) large language model developed by Alibaba Cloud Tongyi Qianwen team within the Qwen3 series. It has 235 billion total parameters with 22 billion activated per inference. Released as an update to the non-thinking mode Qwen3-235B-A22B, it focuses on significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, programming, and tool usage. Additionally, it enhances coverage of multilingual long-tail knowledge and better aligns with user preferences in subjective and open-ended tasks to generate more helpful and higher-quality text."
},
"Qwen/Qwen3-235B-A22B-Thinking-2507": {
"description": "Qwen3-235B-A22B-Thinking-2507 is a member of the Qwen3 large language model series developed by Alibaba Tongyi Qianwen team, specializing in complex reasoning tasks. Based on a mixture-of-experts (MoE) architecture with 235 billion total parameters and approximately 22 billion activated per token, it balances strong performance with computational efficiency. As a dedicated “thinking” model, it significantly improves performance in logic reasoning, mathematics, science, programming, and academic benchmarks requiring human expertise, ranking among the top open-source thinking models. It also enhances general capabilities such as instruction following, tool usage, and text generation, natively supports 256K long-context understanding, and is well-suited for scenarios requiring deep reasoning and long document processing."
},
"Qwen/Qwen3-30B-A3B": {
"description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functions, and multilingual support, with a switchable thinking mode."
},
"Qwen/Qwen3-30B-A3B-Instruct-2507": {
"description": "Qwen3-30B-A3B-Instruct-2507 is an updated version of the Qwen3-30B-A3B non-thinking mode. It is a Mixture of Experts (MoE) model with a total of 30.5 billion parameters and 3.3 billion active parameters. The model features key enhancements across multiple areas, including significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. Additionally, it has made substantial progress in covering long-tail multilingual knowledge and better aligns with user preferences in subjective and open-ended tasks, enabling it to generate more helpful responses and higher-quality text. Furthermore, its long-text comprehension capability has been extended to 256K tokens. This model supports only the non-thinking mode and does not generate `<think></think>` tags in its output."
},
"Qwen/Qwen3-32B": {
"description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functions, and multilingual support, with a switchable thinking mode."
},
"Qwen/Qwen3-8B": {
"description": "Qwen3 is a next-generation model with significantly enhanced capabilities, achieving industry-leading levels in reasoning, general tasks, agent functions, and multilingual support, with a switchable thinking mode."
},
"Qwen2-72B-Instruct": {
"description": "Qwen2 is the latest series of the Qwen model, supporting 128k context. Compared to the current best open-source models, Qwen2-72B significantly surpasses leading models in natural language understanding, knowledge, coding, mathematics, and multilingual capabilities."
},
"Qwen2-7B-Instruct": {
"description": "Qwen2 is the latest series of the Qwen model, capable of outperforming optimal open-source models of similar size and even larger models. Qwen2 7B has achieved significant advantages in multiple evaluations, especially in coding and Chinese comprehension."
},
"Qwen2-VL-72B": {
"description": "Qwen2-VL-72B is a powerful visual language model that supports multimodal processing of images and text, capable of accurately recognizing image content and generating relevant descriptions or answers."
},
"Qwen2.5-14B-Instruct": {
"description": "Qwen2.5-14B-Instruct is a large language model with 14 billion parameters, delivering excellent performance, optimized for Chinese and multilingual scenarios, and supporting applications such as intelligent Q&A and content generation."
},
"Qwen2.5-32B-Instruct": {
"description": "Qwen2.5-32B-Instruct is a large language model with 32 billion parameters, offering balanced performance, optimized for Chinese and multilingual scenarios, and supporting applications such as intelligent Q&A and content generation."
},
"Qwen2.5-72B-Instruct": {
"description": "Qwen2.5-72B-Instruct supports 16k context and generates long texts exceeding 8K. It enables seamless interaction with external systems through function calls, greatly enhancing flexibility and scalability. The model's knowledge has significantly increased, and its coding and mathematical abilities have been greatly improved, with multilingual support for over 29 languages."
},
"Qwen2.5-7B-Instruct": {
"description": "Qwen2.5-7B-Instruct is a large language model with 7 billion parameters, supporting function calls and seamless interaction with external systems, greatly enhancing flexibility and scalability. It is optimized for Chinese and multilingual scenarios, supporting applications such as intelligent Q&A and content generation."
},
"Qwen2.5-Coder-14B-Instruct": {
"description": "Qwen2.5-Coder-14B-Instruct is a large-scale pre-trained programming instruction model with strong code understanding and generation capabilities, efficiently handling various programming tasks, particularly suited for intelligent code writing, automated script generation, and programming problem-solving."
},
"Qwen2.5-Coder-32B-Instruct": {
"description": "Qwen2.5-Coder-32B-Instruct is a large language model specifically designed for code generation, code understanding, and efficient development scenarios, featuring an industry-leading 32 billion parameters to meet diverse programming needs."
},
"Qwen3-235B": {
"description": "Qwen3-235B-A22B is a Mixture of Experts (MoE) model that introduces a \"Hybrid Reasoning Mode,\" allowing users to seamlessly switch between \"Thinking Mode\" and \"Non-Thinking Mode.\" It supports understanding and reasoning in 119 languages and dialects and possesses powerful tool invocation capabilities. In comprehensive benchmarks covering overall ability, coding and mathematics, multilingual proficiency, knowledge, and reasoning, it competes with leading large models on the market such as DeepSeek R1, OpenAI o1, o3-mini, Grok 3, and Google Gemini 2.5 Pro."
},
"Qwen3-32B": {
"description": "Qwen3-32B is a dense model that introduces a \"Hybrid Reasoning Mode,\" enabling users to seamlessly switch between \"Thinking Mode\" and \"Non-Thinking Mode.\" Thanks to architectural improvements, increased training data, and more efficient training methods, its overall performance is comparable to that of Qwen2.5-72B."
},
"SenseChat": {
"description": "Basic version model (V4) with a context length of 4K, featuring strong general capabilities."
},
"SenseChat-128K": {
"description": "Basic version model (V4) with a context length of 128K, excelling in long text comprehension and generation tasks."
},
"SenseChat-32K": {
"description": "Basic version model (V4) with a context length of 32K, flexibly applicable to various scenarios."
},
"SenseChat-5": {
"description": "The latest version model (V5.5) with a context length of 128K shows significant improvements in mathematical reasoning, English conversation, instruction following, and long text comprehension, comparable to GPT-4o."
},
"SenseChat-5-1202": {
"description": "Based on version V5.5, this latest release shows significant improvements over the previous version in foundational Chinese and English capabilities, chat, science knowledge, humanities knowledge, writing, mathematical logic, and word count control."
},
"SenseChat-5-Cantonese": {
"description": "With a context length of 32K, it surpasses GPT-4 in Cantonese conversation comprehension and is competitive with GPT-4 Turbo in knowledge, reasoning, mathematics, and code writing across multiple domains."
},
"SenseChat-5-beta": {
"description": "Partially outperforms SenseCat-5-1202"
},
"SenseChat-Character": {
"description": "Standard version model with an 8K context length and high response speed."
},
"SenseChat-Character-Pro": {
"description": "Advanced version model with a context length of 32K, offering comprehensive capability enhancements and supporting both Chinese and English conversations."
},
"SenseChat-Turbo": {
"description": "Suitable for fast question answering and model fine-tuning scenarios."
},
"SenseChat-Turbo-1202": {
"description": "This is the latest lightweight version model, achieving over 90% of the full model's capabilities while significantly reducing inference costs."
},
"SenseChat-Vision": {
"description": "The latest version model (V5.5) supports multi-image input and fully optimizes the model's basic capabilities, achieving significant improvements in object attribute recognition, spatial relationships, action event recognition, scene understanding, emotion recognition, logical reasoning, and text understanding and generation."
},
"SenseNova-V6-5-Pro": {
"description": "With comprehensive updates to multimodal, language, and reasoning data, along with optimized training strategies, the new model achieves significant improvements in multimodal reasoning and generalized instruction-following capabilities. It supports a context window of up to 128K tokens and excels in specialized tasks such as OCR and cultural tourism IP recognition."
},
"SenseNova-V6-5-Turbo": {
"description": "With comprehensive updates to multimodal, language, and reasoning data, along with optimized training strategies, the new model achieves significant improvements in multimodal reasoning and generalized instruction-following capabilities. It supports a context window of up to 128K tokens and excels in specialized tasks such as OCR and cultural tourism IP recognition."
},
"SenseNova-V6-Pro": {
"description": "Achieves a native unification of image, text, and video capabilities, breaking through the limitations of traditional discrete multimodality, winning dual championships in the OpenCompass and SuperCLUE evaluations."
},
"SenseNova-V6-Reasoner": {
"description": "Balances visual and linguistic deep reasoning, enabling slow thinking and profound inference, presenting a complete chain of thought process."
},
"SenseNova-V6-Turbo": {
"description": "Achieves a native unification of image, text, and video capabilities, breaking through the limitations of traditional discrete multimodality, leading comprehensively in core dimensions such as multimodal foundational abilities and linguistic foundational abilities, excelling in both literature and science, and consistently ranking among the top tier in various assessments both domestically and internationally."
},
"Skylark2-lite-8k": {
"description": "Skylark 2nd generation model, Skylark2-lite model is characterized by high response speed, suitable for high real-time requirements, cost-sensitive scenarios, and situations where model accuracy is less critical, with a context window length of 8k."
},
"Skylark2-pro-32k": {
"description": "Skylark 2nd generation model, Skylark2-pro version has high model accuracy, suitable for more complex text generation scenarios such as professional field copy generation, novel writing, and high-quality translation, with a context window length of 32k."
},
"Skylark2-pro-4k": {
"description": "Skylark 2nd generation model, Skylark2-pro model has high model accuracy, suitable for more complex text generation scenarios such as professional field copy generation, novel writing, and high-quality translation, with a context window length of 4k."
},
"Skylark2-pro-character-4k": {
"description": "Skylark 2nd generation model, Skylark2-pro-character has excellent role-playing and chat capabilities, adept at engaging in conversations with users based on their prompt requests, showcasing distinct character styles and flowing dialogue, making it well-suited for building chatbots, virtual assistants, and online customer service, with high response speed."
},
"Skylark2-pro-turbo-8k": {
"description": "Skylark 2nd generation model, Skylark2-pro-turbo-8k provides faster inference at a lower cost, with a context window length of 8k."
},
"THUDM/GLM-4-32B-0414": {
"description": "GLM-4-32B-0414 is the next-generation open-source model in the GLM series, boasting 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series."
},
"THUDM/GLM-4-9B-0414": {
"description": "GLM-4-9B-0414 is a small model in the GLM series, with 9 billion parameters. This model inherits the technical characteristics of the GLM-4-32B series while providing a more lightweight deployment option. Despite its smaller size, GLM-4-9B-0414 still demonstrates excellent capabilities in tasks such as code generation, web design, SVG graphics generation, and search-based writing."
},
"THUDM/GLM-4.1V-9B-Thinking": {
"description": "GLM-4.1V-9B-Thinking is an open-source vision-language model (VLM) jointly released by Zhipu AI and Tsinghua University's KEG Lab, designed specifically for handling complex multimodal cognitive tasks. Based on the GLM-4-9B-0414 foundation model, it significantly enhances cross-modal reasoning ability and stability by introducing the Chain-of-Thought reasoning mechanism and employing reinforcement learning strategies."
},
"THUDM/GLM-Z1-32B-0414": {
"description": "GLM-Z1-32B-0414 is a reasoning model with deep thinking capabilities. This model is developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, with further training in mathematics, coding, and logic tasks. Compared to the base model, GLM-Z1-32B-0414 significantly enhances mathematical abilities and the capacity to solve complex tasks."
},
"THUDM/GLM-Z1-9B-0414": {
"description": "GLM-Z1-9B-0414 is a small model in the GLM series, with only 9 billion parameters, yet it demonstrates remarkable capabilities while maintaining the open-source tradition. Despite its smaller size, this model excels in mathematical reasoning and general tasks, leading the performance among similarly sized open-source models."
},
"TH