模型概览
模型规格
- 上下文
- 32,000 tokens
- 最大输出
- 28,800 tokens
- 架构
- text->embeddings
- 分词器
- Other
- 知识截止时间
- 暂未提供
- 内容审核
- 否
能力与模态
输入 → 输出
输入
text
输出
embeddings
API
支持的 API 参数
可用提供方
1 提供方
VoyageAI by MongoDB
unknown- 上下文
- 32K
- 最大输出
- 29K
- 输入
- $0.020
- 输出
- 免费
- 缓存读取
- 暂未提供
- 缓存写入
- 暂未提供
voyageai
VoyageAI by MongoDB: voyage-4-lite
voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions,...
voyageai
个模型
voyageai
VoyageAI by MongoDB: voyage-code-4
voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048, 1024, 512, and 256...
- 上下文
- 32K
- 输入
- text
- 输出
- embeddings
输入: $0.120输出: 免费每百万 Token
查看模型详情 →voyageai
VoyageAI by MongoDB: rerank-2.5-lite
rerank-2.5-lite is a reranker optimized for both latency and quality, delivering a 7.16% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5...
- 上下文
- 32K
- 输入
- text
- 输出
- rerank
输入: 免费输出: 免费每百万 Token
查看模型详情 →voyageai
VoyageAI by MongoDB: rerank-2.5
rerank-2.5 is a cutting-edge reranker optimized for quality, delivering a 7.94% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 12.70%...
- 上下文
- 32K
- 输入
- text
- 输出
- rerank
输入: 免费输出: 免费每百万 Token
查看模型详情 →voyageai
VoyageAI by MongoDB: voyage-multimodal-3.5
voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for...
- 上下文
- 32K
- 输入
- text · image
- 输出
- embeddings
输入: $0.120输出: 免费每百万 Token
查看模型详情 →