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 |
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Voyage 4 lite
Voyage AI
|
$0.02 | - | $0 | per 1M tokens |
|
Voyage code 2
This model
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage code 3
Voyage AI
|
$0.18 | - | $0 | per 1M tokens |
|
Voyage context 3
Voyage AI
|
$0.18 | - | $0 | per 1M tokens |
|
Voyage finance 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage law 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage multilingual 2
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage multimodal 3
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
|
Voyage multimodal 3.5
Voyage AI
|
$0.12 | - | $0 | per 1M tokens |
Voyage code 2
By Voyage AI
voyage-code-2 is a text embedding model designed for code-related applications including semantic code search, code completion, and code assistant tools. With a 16,000-token context window and 1536-dimensional embeddings, it handles diverse programming languages including Python, C++, Java, and ML frameworks such as NumPy, PyTorch, and TensorFlow. Trained on large-scale code datasets using advanced loss functions and contrastive pair techniques, it achieves 14.52% recall improvement over competitors on 11 code retrieval benchmarks derived from HumanEval, MBPP, APPS, DS-1000, Codechef, and LeetCode. It also exceeds OpenAI embeddings by 3.03% on general-purpose text datasets. Accessible via the Voyage AI API and Python client, it is optimized for production environments with low inference latency and high throughput, making it suitable for RAG pipelines and large-scale semantic search systems.
Coding
Gen 2
Overview
voyage-code-2 is an embedding model optimized for semantic code retrieval. It supports a 16,000-token context window and produces 1536-dimensional vector embeddings. Trained on large-scale code datasets using advanced loss functions and contrastive learning, it outperforms competing models on code retrieval benchmarks and also improves on general-purpose text retrieval tasks.
Pricing
Compare Voyage code 2 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
This model
Voyage AI
|
$0.0804 | - | $0 | per 1M tokens |
|
Voyage code 3
Voyage AI
|
$0.1206 | - | $0 | per 1M tokens |
|
Voyage context 3
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 |
|
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 23, 2026
