Wispr Flow - Don’t type, just speak
Mini tools| Model | Input | Cached input | Output | Unit |
|---|---|---|---|---|
|
rerank 2.5
Voyage AI
|
$0.05 | - | $0 | per 1M tokens |
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Rerank 2.5 lite
Voyage AI
|
$0.02 | - | $0 | per 1M tokens |
|
Voyage 4
Voyage AI
|
$0.06 | - | $0 | per 1M tokens |
|
Voyage 4 large
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage 4 lite
Voyage AI
|
$0.02 | - | $0 | per 1M tokens |
|
Voyage code 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage code 3
Voyage AI
|
$0.18 | - | $0 | per 1M tokens |
|
Voyage context 3
This model
Voyage AI
|
$0.18 | - | $0 | per 1M tokens |
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Voyage finance 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
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Voyage law 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
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Voyage multilingual 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
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Voyage multimodal 3
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
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Voyage multimodal 3.5
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
Voyage context 3
By Voyage AI
voyage-context-3 is a contextualized chunk embedding model for RAG and retrieval systems. Unlike standard embedding models that treat chunks in isolation, it processes the full document in a single forward pass and generates a distinct embedding per chunk encoding both local chunk content and coarse-grained document-level context. This eliminates the need for manual context augmentation such as overlapping chunks, metadata prepending, or LLM-based contextual retrieval. It accepts the same chunk inputs as standard embedding models and produces vectors with identical output dimensions, making it a drop-in replacement in existing pipelines without downstream workflow changes. The model supports embedding dimensions of 256, 512, 1024, and 2048 via Matryoshka learning, and quantization levels of float32, int8, uint8, and binary via quantization-aware training. Per-chunk context window is 32,000 tokens; total document context length is 120,000 tokens. Designed for long unstructured documents, cross-chunk reasoning tasks, and high-sensitivity retrieval in domains such as finance, legal, medical, and technical documentation. Accessible via the Voyage AI API.
Text
Gen 7
Overview
voyage-context-3 is a contextualized chunk embedding model that produces vectors capturing both chunk-level detail and full document context. It processes entire documents in a single pass and generates a distinct embedding per chunk. Supports flexible output dimensions (256, 512, 1024, 2048) via Matryoshka learning and multiple quantization options (float32, int8, uint8, binary). Per-chunk context window is 32K tokens; total document context is 120K tokens.
Pricing
Compare Voyage context 3 with other models listed in the same vendor pricing tiers and context lengths.
Tier
Standard
Batch
| Model | Input | Cached input | Output | Unit |
|---|---|---|---|---|
|
rerank 2.5
Voyage AI
|
$0.0335 | - | $0 | per 1M tokens |
|
Rerank 2.5 lite
Voyage AI
|
$0.0134 | - | $0 | per 1M tokens |
|
Voyage 4
Voyage AI
|
$0.0402 | - | $0 | per 1M tokens |
|
Voyage 4 large
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage 4 lite
Voyage AI
|
$0.0134 | - | $0 | per 1M tokens |
|
Voyage code 2
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage code 3
Voyage AI
|
$0.1206 | - | $0 | per 1M tokens |
|
Voyage context 3
This model
Voyage AI
|
$0.1206 | - | $0 | per 1M tokens |
|
Voyage finance 2
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage law 2
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
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Voyage multilingual 2
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage multimodal 3
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage multimodal 3.5
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
About Voyage AI
Voyage AI provides best-in-class embedding models and rerankers for search and retrieval over unstructured data, used to power retrieval-augmented generation (RAG) and AI applications. It offers general-purpose, domain-specific (finance, legal, code) and company-specific fine-tuned models. Founded in 2023 and based in Palo Alto, the company was acquired by MongoDB, Inc. in February 2025 and now operates as a MongoDB subsidiary.
View Company Profile
Last updated: June 24, 2026
