Gemini Embedding 001 pricing is currently set at $0.15 per 1 million input tokens. As the first major text embedding model in the Gemini family, it established a competitive baseline for developers looking to integrate semantic search and retrieval-augmented generation (RAG) into their applications. However, current market data and technical roadmaps indicate that Gemini Embedding 001 is now classified as a legacy model, superseded by more advanced versions such as Gemini Embedding 2.

Understanding the pricing structure of this specific model requires a dual look at its direct token costs and the indirect costs associated with its legacy status, including potential migration requirements and the technical limitations of its 2,048-token context window.

Direct Pricing Breakdown for Gemini Embedding 001

The cost efficiency of Gemini Embedding 001 was one of its primary selling points during its general availability phase. For production environments managed via Vertex AI or the paid tier of Google AI Studio, the billing is straightforward:

  • Standard Rate: $0.15 per 1,000,000 input tokens.
  • Output Costs: Not applicable. Since embedding models generate vector representations (arrays of numbers) rather than text, there are no "output token" charges.
  • Free Tier Availability: Google AI Studio provides a generous free tier for prototyping, allowing up to 1,500 requests per day for embedding models without a credit card requirement. However, usage in this tier may be subject to data logging for model improvement.

In contrast, Enterprise users accessing the model through Vertex AI pay the $0.15 rate but benefit from enterprise-grade security, data privacy (where data is not used for training), and higher rate limits.

Technical Specifications and Cost Performance

The value of the $0.15/1M token price point must be weighed against the model's technical capabilities. Gemini Embedding 001 supports a maximum input of 2,048 tokens. For many RAG applications, this is sufficient for chunked documents, but it lacks the depth required for long-form analysis compared to newer 8k or 32k context models.

Matryoshka Representation Learning (MRL) and Storage Savings

One of the most significant cost-saving features of Gemini Embedding 001 is its support for Matryoshka Representation Learning. This technique allows developers to truncate the output vector dimensions without a linear loss in accuracy.

The model defaults to 3,072 dimensions. However, our internal testing shows that you can scale down to 768 or 1,536 dimensions. While the input token price of $0.15 remains the same, reducing the dimensions drastically lowers the downstream storage costs in vector databases like Pinecone, Milvus, or Weaviate. For high-volume applications storing billions of vectors, reducing dimensions by 75% can lead to thousands of dollars in monthly infrastructure savings.

Multi-language Efficiency

Supporting over 100 languages, Gemini Embedding 001 offers a unified pricing model for multilingual data. Unlike some legacy systems that require separate models or higher premiums for non-English scripts, the $0.15 rate applies across all supported languages, making it a cost-effective choice for global deployments involving scientific, legal, or financial documentation.

Why Gemini Embedding 001 is Moving to Legacy Status

As of 2026, the transition from Gemini Embedding 001 to Gemini Embedding 2 is well underway. The newer model is priced at approximately $0.20 per 1 million tokens, a slight premium that reflects its expanded capabilities.

The legacy status of version 001 implies several critical points for developers:

  1. Limited Future Updates: No further performance enhancements or language expansions are expected for the 001 version.
  2. Quota Constraints: Google often prioritizes hardware resources for current-generation models. Users of 001 may face tighter rate limits or longer wait times for quota increases compared to users of Embedding 2.
  3. Deprecation Risks: While legacy models typically remain available via specific versioned endpoints for a set period, they are ultimately scheduled for retirement.

Hidden Costs of Remaining on a Legacy Model

While $0.15 per million tokens sounds like a bargain, staying on Gemini Embedding 001 might incur "technical debt" costs.

The Re-indexing Hurdle

The most significant hidden cost in embedding workflows is re-indexing. Vector embeddings are not interchangeable between different models. If you generate 10 million vectors using Gemini Embedding 001 and later decide to move to Gemini Embedding 2, you cannot simply query the old vectors with the new model.

You must re-process your entire dataset through the new model. At $0.20/1M tokens for the new model, a migration for a 1-billion token dataset would cost $200 in API fees alone, plus the compute costs for the migration script and the downtime or parallel storage costs during the transition.

Context Window Bottlenecks

The 2,048-token limit of 001 requires more aggressive document chunking. More chunks mean more rows in your database and more API calls to find relevant context. Newer models with 8,192 tokens allow for larger, more coherent chunks, which often results in better retrieval accuracy and potentially fewer total API calls in complex reasoning tasks.

Gemini Embedding 001 vs. The Competition

To understand if $0.15/1M tokens is competitive, we must look at the broader landscape of proprietary embedding models available in 2025 and 2026.

Model Price per 1M Tokens Max Context Status
Gemini Embedding 001 $0.15 2,048 Legacy
Gemini Embedding 2 $0.20 8,192 Current
OpenAI text-embedding-3-small $0.02 8,191 Current
OpenAI text-embedding-3-large $0.13 8,191 Current
Google Text-Embedding-005 $0.00625 2,048 Current (Optimized)

Interestingly, Google's own text-embedding-005 model offers a significantly lower price point ($0.00625/1M tokens) for basic text tasks. This suggests that Gemini Embedding 001 occupies a middle ground—offering better semantic performance than the basic text models but lacking the multimodal power of the newer Gemini 2 series.

Implementation in BigQuery ML

For data warehouse users, Gemini Embedding 001 is accessible via BigQuery ML. The billing here can sometimes be presented as $0.00012 per 1,000 tokens. This is mathematically equivalent to $0.12 per million tokens in some regional configurations, though standard Vertex AI pricing of $0.15 is the most common benchmark.

When using BigQuery for batch embedding generation, the primary cost consideration is the "Tokens Per Minute" (TPM) quota. Large-scale jobs processing 38 million rows (as seen in public dataset benchmarks) can be completed in a few hours, but users must manage their budget to avoid spikes in Vertex AI billing.

How to Check Your Current Billing and Usage

Because pricing can vary by region (e.g., us-central1 vs. europe-west1), developers should not rely solely on headline figures.

  1. Google Cloud Console: Navigate to the Billing section and filter by "Vertex AI" and the specific SKU for Gemini Embeddings.
  2. Pricing Calculator: Use the Google Cloud Pricing Calculator, selecting "Vertex AI" and then the "Embeddings" tab to input your expected monthly token volume.
  3. AI Studio Monitoring: For those using the API key-based approach, the Google AI Studio dashboard provides a "Plan" section where you can toggle between the Free and Pay-as-you-go tiers.

Conclusion

Gemini Embedding 001 remains a functional and cost-predictable choice at $0.15 per million tokens, but its window of utility is closing. It is best suited for legacy systems that are already indexed and do not require the multimodal or long-context capabilities of modern AI.

For new projects, the marginal cost increase to Gemini Embedding 2 ($0.20/1M) is almost always justified by the superior context window and the avoidance of future re-indexing costs. If pure cost-minimization is the goal, Google's text-embedding-005 offers a much lower entry point at a fraction of the price.

Summary of Key Pricing Points

  • Cost: $0.15 per 1 million input tokens.
  • Status: Legacy (Migration to version 2 recommended).
  • Dimensions: 3,072 (Supports truncation via Matryoshka learning).
  • Free Tier: 1,500 requests per day in Google AI Studio.
  • Context: 2,048 tokens.

FAQ

Is Gemini Embedding 001 still available for new projects?

Yes, it is currently available, but Google recommends starting all new development with Gemini Embedding 2 to ensure longer support cycles and better performance.

Can I mix vectors from 001 and 002 in the same database?

No. Each model creates its own unique mathematical vector space. Mixing them will result in nonsensical retrieval results. You must use one model for your entire index and query process.

Does Gemini Embedding 001 charge for output tokens?

No, there are no charges for output tokens with this model, as the output is a vector array rather than generated text.

How does the Matryoshka technique affect pricing?

It does not change the API cost per token ($0.15), but it significantly reduces your storage and compute costs for the vector database where you store the results.

When will Gemini Embedding 001 be fully deprecated?

Based on current lifecycle policies, legacy models typically see support end 6-12 months after a successor reaches general availability. Developers should check their Google Cloud Console for specific deprecation notices.