- Input
- $0.02
- Output
- $0.00
voyageaiListed Jul 27, 2026
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,...
Context window32K
Max output28.8K
Provider endpoints1
Benchmark results0
Measured performance
All leaderboards ↗Benchmark results
No published results in this index yet
Scores appear here when an evaluation matches this exact model.
Access & cost
1 quotePrices across providers
Model specifications
- Developer
- voyageai
- Modality
- Embedding
- Context
- 32,000 tokens
- Maximum output
- 28,800 tokens
- Weights
- Not reported
- Provider listed
- Jul 27, 2026
- First discovered
- Oct 16, 2026
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View all ↗voyageaiListed Aug 13
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...
32K contextNo reported score
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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…
32K contextImage inputNo reported score
No reported score
voyageaiListed Jul 27
VoyageAI by MongoDB: voyage-4
voyage-4 is a general-purpose (including multilingual) embedding model optimized for retrieval/search and AI applications. voyage-4 supports embeddings in 2048, 1024, 512, and 256 dimensions, with multiple…
32K contextNo reported score
No reported score
voyageaiListed Jul 27
VoyageAI by MongoDB: voyage-4-large
voyage-4-large is a state-of-the-art general-purpose and multilingual embedding optimized for retrieval quality. Enabled by Matryoshka learning and quantization-aware training, voyage-4-large supports…
32K contextNo reported score
No reported score