MODEL PROFILE · 模型档案

MOSS-Audio-4B-Instruct

OpenMOSS-Team · MOSS-Audio-4B-Instruct

MOSS-Audio is an open-source **audio understanding model** from MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute. It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**. In this release, we provide **four models**: **MOSS-Audio-4B-Instruct**, **MOSS-Audio-4B-Thinking**, **MOSS-Audio-8B-Instruct**, and **MOSS-Audio-8B-Thinking**. The Instruct variants are optimized for direct instruction following, while the Thinking variants provide stronger chain-of-thought reasoning capabilities. Understanding audio requires more than simply transcribing words — it demands the ability to perceive acoustic cues, recognize speakers and emotions, interpret environmental sounds, reason over temporal context, and handle complex multi-step inference. **MOSS-Audio** is built to unify these capabilities within a single model. MOSS-Audio follows a modular design comprising three components: an audio encoder, a modality adapter, and a large language model. Raw audio is first encoded by **MOSS-Audio-Encoder** into continuous temporal representations at **12.5 Hz**, which are then projected into the language model's embedding space through the adapter and finally consumed by the LLM for auto-regressive text generation.

译文排队中,当前展示来源原文。

语音识别multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出音频、文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:15:33
OVERVIEW

模型介绍

MOSS-Audio is an open-source **audio understanding model** from MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute. It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**. In this release, we provide **four models**: **MOSS-Audio-4B-Instruct**, **MOSS-Audio-4B-Thinking**, **MOSS-Audio-8B-Instruct**, and **MOSS-Audio-8B-Thinking**. The Instruct variants are optimized for direct instruction following, while the Thinking variants provide stronger chain-of-thought reasoning capabilities. Understanding audio requires more than simply transcribing words — it demands the ability to perceive acoustic cues, recognize speakers and emotions, interpret environmental sounds, reason over temporal context, and handle complex multi-step inference. **MOSS-Audio** is built to unify these capabilities within a single model. MOSS-Audio follows a modular design comprising three components: an audio encoder, a modality adapter, and a large language model. Raw audio is first encoded by **MOSS-Audio-Encoder** into continuous temporal representations at **12.5 Hz**, which are then projected into the language model's embedding space through the adapter and finally consumed by the LLM for auto-regressive text generation.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。