Enum Class DashScopeModel.ChatModel
java.lang.Object
java.lang.Enum<DashScopeModel.ChatModel>
com.alibaba.cloud.ai.dashscope.spec.DashScopeModel.ChatModel
- All Implemented Interfaces:
Serializable,Comparable<DashScopeModel.ChatModel>,Constable,org.springframework.ai.model.ChatModelDescription,org.springframework.ai.model.ModelDescription
- Enclosing class:
- DashScopeModel
public static enum DashScopeModel.ChatModel
extends Enum<DashScopeModel.ChatModel>
implements org.springframework.ai.model.ChatModelDescription
Spring AI Alibaba DashScope implements all models that support the dashscope
platform, and only the Qwen series models are listed here. For more model options,
refer to: Model List
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Nested Class Summary
Nested classes/interfaces inherited from class java.lang.Enum
Enum.EnumDesc<E extends Enum<E>> -
Enum Constant Summary
Enum ConstantsEnum ConstantDescriptionQVQ is a visual reasoning model that supports visual input and generates thought chains.The QwQ inference model trained based on the Qwen2.5-32B model greatly improves the model inference ability through reinforcement learning.Tongyi Qianwen Code Model.The Tongyi Qianwen series is a model with the longest context window, balanced capabilities and low cost.The Tongyi Qianwen mathematical model is a language model specifically designed for solving mathematical problems.The model supports an 8k tokens context, and to ensure normal use and output, the API limits user input to 6k tokens.The model supports a context of 30k tokens.The Qwen-Omni model can receive combined inputs of various modalities such as text, images, audio, and video, and generate responses in text or voice forms.Compared to the Omniverse model, it supports audio-based streaming input and has a built-in VAD (Voice Activity Detection) function, which can automatically detect the start and end of the user's voice.The QWEN-OMNI series models support the input of multiple modalities of data, including video, audio, image, text, and output audio and text qwen-omniTongyi Qianwen new multi-modal understanding generation model, supports text, image, voice, video input understanding and mixed input understanding, with text and voice simultaneous streaming generation ability, multi-modal content understanding speed significantly improved, provides 4 natural dialogue tones, this version is a dynamic update version.The capabilities are balanced, with the reasoning effect, cost and speed falling between that of Qwen Max and Qwen Flash.The model supports a context of 32k tokens.The qwen-vl model can answer based on the pictures you pass in.The Tongyi Qianwen OCR model is a model specifically designed for text extraction.New multi-modal understanding generation large model trained based on Qwen2.5, supports text, image, voice, and video input understanding as well as mixed input understanding, with text and voice simultaneous streaming generation capabilities.The best-performing model in the Qwen series, suitable for complex and multi-step tasks.Tongyi Qianwen 3-Omni-Flash multimodal large model, based on Thinker-Talker Mixture of Experts (MoE) architecture, supports efficient understanding of text, images, audio, video and speech generation capability, and can perform text interaction in 119 languages and speech interaction in 20 languages to generate human-like speech for accurate cross-language communication.Tongyi Qianwen VL is a text generation model with visual (image) comprehension capabilities.Tongyi Qianwen MT Plus is a multilingual language model that supports.The QwQ inference model trained based on the Qwen2.5 model has significantly enhanced the model's inference capabilities through reinforcement learning. -
Field Summary
Fields -
Method Summary
Modifier and TypeMethodDescriptiongetName()getValue()static DashScopeModel.ChatModelReturns the enum constant of this class with the specified name.static DashScopeModel.ChatModel[]values()Returns an array containing the constants of this enum class, in the order they are declared.Methods inherited from class java.lang.Enum
clone, compareTo, describeConstable, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOfMethods inherited from interface org.springframework.ai.model.ModelDescription
getDescription, getVersion
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Enum Constant Details
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QWEN_PLUS
The capabilities are balanced, with the reasoning effect, cost and speed falling between that of Qwen Max and Qwen Flash. It is suitable for medium-complex tasks. -
QWEN_TURBO
The model supports a context of 32k tokens. To ensure normal use and output, the API limits user input to 30k tokens. -
QWEN_MAX
The model supports an 8k tokens context, and to ensure normal use and output, the API limits user input to 6k tokens. -
QWEN3_MAX
The best-performing model in the Qwen series, suitable for complex and multi-step tasks. -
QWEN_LONG
The Tongyi Qianwen series is a model with the longest context window, balanced capabilities and low cost. It is suitable for tasks such as long text analysis, information extraction, summary and abstract generation, and classification tagging. -
QWN_MT_PLUS
Tongyi Qianwen MT Plus is a multilingual language model that supports. Belongs to Qwen3-MT. -
QWEN_MATH_PLUS
The Tongyi Qianwen mathematical model is a language model specifically designed for solving mathematical problems. -
QWEN_CODER_PLUS
Tongyi Qianwen Code Model. The latest Qwen3-Coder-Plus series models are code generation models based on Qwen3, featuring powerful Coding Agent capabilities. They excel at tool invocation and environment interaction, enabling autonomous programming. Their code capabilities are outstanding while also possessing general capabilities. -
QWEN_MAX_LONGCONTEXT
The model supports a context of 30k tokens. To ensure normal use and output, the API limits user input to 28k tokens. -
QWQ_PLUS
The QwQ inference model trained based on the Qwen2.5 model has significantly enhanced the model's inference capabilities through reinforcement learning. The core indicators of the model's mathematical code (AIME 24/25, LiveCodeBench) as well as some general indicators (IFEval, LiveBench, etc.) have reached the full health level of DeepSeek-R1. qwen3 -
QWEN_3_32B
The QwQ inference model trained based on the Qwen2.5-32B model greatly improves the model inference ability through reinforcement learning. The core indicators such as the mathematical code of the model (AIME 24/25, LiveCodeBench) and some general indicators (IFEval, LiveBench, etc.) have reached the level of DeepSeek-R1 full blood version, and all indicators significantly exceed the DeepSeek-R1-Distill-Qwen-32B, which is also based on Qwen2.5-32B. qwen3 -
QWEN_OMNI_TURBO
The QWEN-OMNI series models support the input of multiple modalities of data, including video, audio, image, text, and output audio and text qwen-omni -
QWEN_OMNI_FLASH_REALTIME
Compared to the Omniverse model, it supports audio-based streaming input and has a built-in VAD (Voice Activity Detection) function, which can automatically detect the start and end of the user's voice. -
QWEN3_OMNI_FLASH
Tongyi Qianwen 3-Omni-Flash multimodal large model, based on Thinker-Talker Mixture of Experts (MoE) architecture, supports efficient understanding of text, images, audio, video and speech generation capability, and can perform text interaction in 119 languages and speech interaction in 20 languages to generate human-like speech for accurate cross-language communication. The model has powerful command following and system prompt customization functions, flexibly adapts dialogue style and role setting, and is widely used in text creation, voice assistant, multimedia analysis and other scenes to provide a natural and smooth multi-modal interaction experience. -
QWEN_OMNI_FLASH
The Qwen-Omni model can receive combined inputs of various modalities such as text, images, audio, and video, and generate responses in text or voice forms. It offers multiple anthropomorphic voices and supports voice output in multiple languages and dialects. It can be applied in scenarios such as text creation, visual recognition, and voice assistants. -
QWEN_OMNI_TURBO_LATEST
Tongyi Qianwen new multi-modal understanding generation model, supports text, image, voice, video input understanding and mixed input understanding, with text and voice simultaneous streaming generation ability, multi-modal content understanding speed significantly improved, provides 4 natural dialogue tones, this version is a dynamic update version. -
QWEN2_5_OMNI_7B
New multi-modal understanding generation large model trained based on Qwen2.5, supports text, image, voice, and video input understanding as well as mixed input understanding, with text and voice simultaneous streaming generation capabilities. Multi-modal content understanding speed is significantly improved, providing 4 natural dialogue tones. -
QWEN_VL_MAX
The qwen-vl model can answer based on the pictures you pass in. qwen-vl -
QWEN3_VL_PLUS
Tongyi Qianwen VL is a text generation model with visual (image) comprehension capabilities. It not only can perform OCR (image text recognition), but also can further summarize and reason, such as extracting attributes from product photos and solving problems based on exercise diagrams, etc. -
QWEN_FLASH
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QWEN_VL_OCR
The Tongyi Qianwen OCR model is a model specifically designed for text extraction. Compared to the Tongyi Qianwen VL model, it focuses more on the text extraction capabilities for types of images such as documents, tables, test questions, and handwritten text. It can recognize multiple languages, including English, French, Japanese, Korean, German, Russian, and Italian, etc. -
QVQ_MAX
QVQ is a visual reasoning model that supports visual input and generates thought chains. It demonstrates stronger capabilities in mathematics, programming, visual analysis, creation, and general tasks. -
DEEPSEEK_R1
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DEEPSEEK_V3
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DEEPSEEK_V3_1
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KIMI_K2
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GLM_4_6
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Field Details
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value
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Method Details
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values
Returns an array containing the constants of this enum class, in the order they are declared.- Returns:
- an array containing the constants of this enum class, in the order they are declared
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valueOf
Returns the enum constant of this class with the specified name. The string must match exactly an identifier used to declare an enum constant in this class. (Extraneous whitespace characters are not permitted.)- Parameters:
name- the name of the enum constant to be returned.- Returns:
- the enum constant with the specified name
- Throws:
IllegalArgumentException- if this enum class has no constant with the specified nameNullPointerException- if the argument is null
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getValue
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getName
- Specified by:
getNamein interfaceorg.springframework.ai.model.ModelDescription
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