사용 가능
Qwen-Audio-3.0-TTS Flash is Alibaba's fast, cost-efficient text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.
- 컨텍스트
- 0
- 입력
- US$15
- 출력
- US$0.00
텍스트
Other 모델 패밀리
Other 패밀리의 선별 모델 4개를 컨텍스트, API 가격, 모달리티, 기능으로 비교합니다.
4개의 수집 모델
모델 디렉토리
현재 4개 모델
사용 가능
Qwen-Audio-3.0-TTS Plus is Alibaba's higher-quality text-to-speech model, generating spoken audio from text via the DashScope Speech Synthesizer API.
- 컨텍스트
- 0
- 입력
- US$20
- 출력
- US$0.00
텍스트
사용 가능
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
- 컨텍스트
- 33K
- 입력
- US$0.02
- 출력
- US$0.00
텍스트
사용 가능
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
- 컨텍스트
- 33K
- 입력
- US$0.01
- 출력
- US$0.00
텍스트
OUR METHOD
AIToolly의 모델 데이터 처리 방식
모델 고유 정보와 제공사 엔드포인트 정보를 분리합니다. 서드파티 카탈로그는 탐색과 스냅샷에 사용하며 모델 정보는 공식 문서와 검증된 모델 카드를 우선합니다.