---
title: "voyage-context-4 Cost Calculator - Voyage"
description: "Calculate the cost of using voyage-context-4 from Voyage. Input costs $0.12 and output $0.0000 per 1M tokens."
url: "https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-context-4"
markdown: "https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-context-4.md"
---

# voyage-context-4 Cost Calculator - Voyage

> Calculate the cost of using voyage-context-4 from Voyage. Input costs $0.12 and output $0.0000 per 1M tokens.

## Important Links

- [View MCP Gateway](https://www.getmaxim.ai/bifrost/resources/mcp-gateway.md)
- [Features](https://www.getmaxim.ai/bifrost/#features)
- [Enterprise](https://www.getmaxim.ai/bifrost/enterprise)
- [Pricing](https://www.getmaxim.ai/bifrost/pricing.md)
- [Docs](https://docs.getbifrost.ai)
- [GitHub](https://github.com/maximhq/bifrost)
- [Book a Demo](https://www.getmaxim.ai/bifrost/book-a-demo)

## About voyage-context-4

voyage-context-4 is an embedding model from Voyage, one of 21 embedding models they offer. It is priced at $0.12 per 1M input tokens and $0.0000 per 1M output tokens, ranking 107 out of 144 embedding models by cost and cheaper than 23% of models in this category. Its 120K-token context window is in the top 6% among embedding models.

## Pricing

Published Voyage pricing for voyage-context-4.

- **$0.12 / 1M tokens Input.**
- **$0.0000 / 1M tokens Output.**

## Technical Specifications

- **Embedding Mode.**
- **120,000 Max Input Tokens.**
- **120,000 Max Tokens.**

## How Pricing Compares

At $0.12 per 1M input tokens and $0.0000 per 1M output tokens, voyage-context-4 ranks 107 out of 144 embedding models by input cost. It is more expensive compared to the median of $0.10 for embedding models, and is cheaper than 23% of models in this category.

voyage-context-4 is one of 21 Voyage embedding models, its 120K-token context window places it in the top 6% of embedding models.

| Model | Provider | Input $/1M | Output $/1M | vs voyage-context-4 |
| --- | --- | --- | --- | --- |
| amazon.nova-2-multimodal-embeddings-v1:0 | AWS Bedrock | $0.14 | $0.0000 | +13% |
| amazon.titan-embed-text-v1 | AWS Bedrock | $0.10 | $0.0000 | -17% |
| amazon.titan-embed-g1-text-02 | AWS Bedrock | $0.10 | $0.0000 | -17% |
| ada | Azure | $0.10 | $0.0000 | -17% |
| text-embedding-3-large | Azure | $0.13 | $0.0000 | +8% |

## More from Voyage

- [voyage-code-2 ($0.12/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-code-2.md)
- [voyage-finance-2 ($0.12/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-finance-2.md)
- [voyage-large-2 ($0.12/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-large-2.md)
- [voyage-law-2 ($0.12/1M input)](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-law-2.md)

## Related Resources

- [All Voyage pricing](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage.md)

## FAQ

### Is voyage-context-4 cheaper than amazon.nova-2-multimodal-embeddings-v1:0?

Yes. voyage-context-4 costs $0.12 per 1M input tokens compared to amazon.nova-2-multimodal-embeddings-v1:0's $0.14 per 1M input tokens, making it 13% more affordable. Both are embedding models and share support for audio input.

### How does voyage-context-4 pricing compare to the average embedding model?

voyage-context-4 input pricing is $0.12 per 1M tokens, which is 20% above the median of $0.10 for embedding models. It ranks 107 out of 144 embedding models by input cost, making it cheaper than 23% of models in this category. For output, it costs $0.0000 per 1M tokens compared to the median of $0.02.

### What makes voyage-context-4 different from other Voyage models?

Among Voyage's 21 embedding models, voyage-context-4 ranks 17 by input cost.

### What are the best alternatives to voyage-context-4?

The most comparable embedding models to voyage-context-4 are: amazon.nova-2-multimodal-embeddings-v1:0 from AWS Bedrock ($0.14/1M input tokens); amazon.titan-embed-text-v1 from AWS Bedrock ($0.10/1M input tokens); amazon.titan-embed-g1-text-02 from AWS Bedrock ($0.10/1M input tokens); ada from Azure ($0.10/1M input tokens). These alternatives were selected based on similar capabilities, pricing, and provider diversity. You can compare any of these models in detail using the Bifrost Model Library.

### How large is voyage-context-4's context window?

voyage-context-4 supports up to 120,000 input tokens, placing it in the top 6% of embedding models by context window size (rank 8 of 129). This makes it suitable for processing long documents, extensive code repositories, or maintaining detailed conversation histories.

### How do I calculate voyage-context-4 costs?

voyage-context-4 is priced based on input and output tokens. Use the interactive calculator at the top of this page to estimate costs for your specific workload. Enter your expected input and output tokens volume and the calculator will show the total cost breakdown. For reference, processing 1M input tokens costs $0.12 and generating 1M output tokens costs $0.0000.

## Related Resources

- [All Voyage pricing](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage.md)
- [LLM cost calculator](https://www.getmaxim.ai/bifrost/llm-cost-calculator.md)
- [Model library](https://www.getmaxim.ai/bifrost/model-library.md)
- [Docs: Bifrost docs](https://docs.getbifrost.ai)
- [GitHub: maximhq/bifrost](https://github.com/maximhq/bifrost)
- [Pricing: Bifrost pricing](https://www.getmaxim.ai/bifrost/pricing.md)
- [Enterprise: Bifrost enterprise](https://www.getmaxim.ai/bifrost/enterprise)
- [Book a Demo: Bifrost demo](https://www.getmaxim.ai/bifrost/book-a-demo)
- [Resources: Bifrost resources](https://www.getmaxim.ai/bifrost/resources.md)

---

*This is a markdown version of [https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-context-4](https://www.getmaxim.ai/bifrost/llm-cost-calculator/provider/voyage/model/voyage-context-4) for AI/LLM consumption.*
