Embeddings API Comparison 2026: OpenAI vs Cohere vs Hugging Face

What You'll Learn

• What embeddings do and why retrieval quality depends on more than one benchmark score.
• How the current OpenAI, Gemini, Cohere, Voyage AI, Jina AI, and Qwen3 options differ.
• Which documented dimensions, context limits, languages, and input controls matter in production.
• How to run a fair evaluation before choosing an API for RAG or semantic search.

1. What Embeddings Are and Why They Matter

An embedding converts content into a numerical vector that represents relationships between concepts. A search system can compare a query vector with document vectors and retrieve items that are close under a chosen similarity measure. This makes embeddings useful for semantic search, retrieval augmented generation, classification, clustering, duplicate detection, and recommendation systems.

An embedding model does not answer a user’s question by itself. It creates a representation that another system can search or compare. A RAG application still needs a document collection, chunking policy, metadata filters, a vector index, a reranking step where appropriate, and a generation model.

Model quality is also only one part of retrieval quality. Chunk length, language, spelling variation, query formulation, metadata, distance metric, and reranking can change the result. A model that performs well on a public benchmark may not be the right choice for a company’s invoices, Bengali news, Hindi support tickets, legal documents, or code repository.

The AI systems explainer covers a different technology topic. In this article, the comparison remains limited to embedding models and the engineering decisions around them.

2. What Changed in the 2026 Provider Set

The legacy article treated a small group of model names and a single MTEB table as if they were a permanent market ranking. The current official documentation shows a more fluid set of options. Google’s documentation now presents Gemini Embedding 2 as a multimodal embedding model and retains Gemini Embedding 001 for text-only use. Voyage’s documentation lists a 4-series as its current model family and labels the 3-series as previous generation.

Cohere’s documentation lists embed-v4.0 for text, images, and mixed text and image inputs such as PDFs. OpenAI documents the text-embedding-3 family with adjustable dimensions. Jina documents multilingual embedding models with task and dimension choices. Qwen’s official model card presents Qwen3-Embedding-8B as an Apache-2.0 open-weight text embedding model.

These updates make a fixed “top 10” list fragile. The right comparison is a dated source check followed by an evaluation on the intended corpus. Price pages and model pages can change independently, so record the date and endpoint used for every decision.

For related technology reading, see the AI search optimization guide and the AI browser analysis. Neither article is a substitute for current vendor documentation.

3. Current Models and Documented Capabilities

The table below records only facts from the official provider pages used for this rewrite. It does not rank the models. A dimension count tells you the vector size, not the retrieval quality for your data. A context limit tells you the maximum model input described by the provider, not the correct chunk size for your application.

4. OpenAI Embeddings for Simple Text Retrieval

OpenAI’s official embedding announcement documents two models in the text-embedding-3 family. The announcement reports that text-embedding-3-small improved its cited average MIRACL score from 31.4% to 44.0% and its cited average MTEB score from 61.0% to 62.3% compared with text-embedding-ada-002. It reports a source-date price of $0.00002 per 1,000 tokens for the small model, which is $0.02 per 1M tokens.

The same announcement describes text-embedding-3-large as a larger model with up to 3,072 dimensions. It reports a source-date price of $0.00013 per 1,000 tokens, which is $0.13 per 1M tokens. These are source-dated figures from the January 25, 2024 announcement. Developers should check the current OpenAI pricing page before using them in a 2026 budget.

A notable feature is the dimensions parameter. OpenAI says developers can shorten embeddings to reduce vector storage and compute. Shortening changes the quality and storage trade-off, so a system should validate the selected dimension instead of assuming that the largest vector is always necessary.

OpenAI is a reasonable candidate when a team wants a familiar text-only API, a simple integration path, and a documented dimension control. It is not automatically the right answer for code, images, video, or a corpus where a domain-specific model wins on recall.

See the official OpenAI embedding announcement and its linked documentation for the source-date figures. Do not copy the legacy article’s claim that one current provider is the universal best pick.

5. Google Gemini Embeddings for Text and Multimodal Search

Google’s current embeddings documentation identifies Gemini Embedding 2 as a multimodal embedding model. It maps text, images, video, audio, and documents into a unified embedding space. Google says this enables cross-modal search, classification, and clustering across over 100 languages. The documentation retains Gemini Embedding 001 for text-only use.

Google documents 3,072 default dimensions for Gemini Embedding 001 and Gemini Embedding 2, with output dimensionality controls such as 768 and 1,536. The Gemini Embedding 2 documentation also lists an overall maximum input limit of 8,192 tokens, a maximum of 6 images per request, audio up to 180 seconds, video up to 120 seconds, and one PDF document up to 6 pages.

Task formatting matters. For Gemini Embedding 001, Google documents task types such as retrieval query, retrieval document, question answering, classification, clustering, semantic similarity, code retrieval query, and fact verification. For Gemini Embedding 2, Google recommends including task instructions in the input format rather than using the older task_type field.

Gemini is a candidate when cross-modal retrieval or Google’s task-specific embedding controls matter. A text-only news search system may still prefer a smaller or cheaper option after testing. The official Google embeddings documentation should be checked for current model versions, limits, and pricing.

6. Cohere for Multilingual and Mixed-Input Workloads

Cohere’s official documentation lists embed-v4.0 for text and images, including mixed text and image inputs such as PDFs. It lists output dimensions of 256, 512, 1,024, and 1,536, with 1,536 as the default, and a maximum context length of 128k tokens.

The same documentation says the multilingual embed model supports over 100 languages and lists Indian languages such as Bengali, Hindi, Gujarati, Kannada, Malayalam, Marathi, Tamil, Telugu, and Urdu. That is useful evidence for multilingual evaluation planning. It does not prove that a model will have equal quality for every language, script, domain, or spelling pattern.

For an Indian-language application, create test sets in the languages and scripts that real users will use. Include transliteration, mixed-language queries, spelling variation, named entities, and documents with local terms. Measure recall and answer support separately. A model can retrieve the right language but still return the wrong document because of chunking or metadata filters.

The official Cohere Embed documentation provides the current model table and supported-language information. It does not provide a basis for stating that Cohere is the best option for all Indian developers.

7. Voyage AI and Jina for Retrieval-Focused Pipelines

Voyage’s official documentation lists the Voyage 4 family as its current general-purpose and multilingual set. It lists voyage-4-large, voyage-4, and voyage-4-lite, as well as voyage-code-4 for code retrieval. It also lists voyage-finance-2 and voyage-law-2 for domain-specific retrieval. The documentation describes 32,000-token context for the 4-series models and output dimensions of 256, 512, 1,024, or 2,048.

Voyage also documents a retrieval-specific input_type parameter. Setting an input as a query or document lets the API prepend a task prompt tailored to retrieval. The documentation says the API can accept up to 1,000 texts in a list, subject to model-specific token limits. These controls can matter more than a broad leaderboard position when a production pipeline has a clear query and document distinction.

Jina’s official model page describes jina-embeddings-v3 as a multilingual text embedding model with up to 8,192-token input length and configurable dimensions as low as 32. Jina’s current product pages should be checked for active endpoint names, price, and model availability before deployment. The article does not repeat the legacy price or ranking claims because they were not verified from a current official pricing page.

Read the Voyage embeddings documentation and the Jina model page. Both providers should be evaluated with the same corpus, query set, vector index, and reranker used for competing models.

8. Qwen3 and Other Open-Weight Options

The official Hugging Face model card for Qwen3-Embedding-8B describes an Apache-2.0 text embedding model. Its specification lists over 100 supported languages, a 32k context length, and up to 4,096 embedding dimensions. The model card also supports customized instructions for different tasks and reports its own evaluation claim of an MTEB score of 70.58 as of June 5, 2025.

The model-card score is not a current universal ranking. It is a dated evaluation claim from the model’s own documentation. The official MTEB leaderboard contains multiple tasks and datasets. A score from one task or aggregate cannot predict an application’s retrieval quality without a matching test set.

Open weights can improve control over data location, deployment, and customization. They do not mean zero cost. Self-hosting can require GPU or CPU capacity, memory, monitoring, batching, autoscaling, model updates, incident response, and engineering time. A hosted API may cost more per token but reduce operational work.

Qwen3-Embedding-8B is a candidate for teams that can operate the model and have a reason to control deployment. It is not automatically cheaper or more accurate after total cost, and the Apache-2.0 license should still be reviewed against the intended use.

9. Pricing Per Million Tokens and Total Cost

“Cheapest embeddings API” is an incomplete question. Token price is only one input. Total cost also includes storage, dimensions, re-embedding after a model change, network transfer, reranking, provider minimums, batch processing, observability, and engineering time.

The official OpenAI announcement provides source-date prices for text-embedding-3-small and text-embedding-3-large. The current Google, Cohere, Voyage, and Jina price can change by model, region, tier, and endpoint. This rewrite does not turn a stale snippet or third-party calculator into a current provider quote.

For a fair budget, estimate monthly input tokens, expected re-embedding volume, vector count, dimension size, and retrieval requests. Then calculate hosted API cost and infrastructure cost separately. If a model supports shorter vectors, test whether the storage reduction changes recall enough to matter.

The OpenAI pricing documentation is a better starting point for a current quote than the legacy table. For other providers, use the provider’s own pricing page and record the date when the calculation was made.

10. Choosing an Embedding API for an Indian AI App

Start with the documents and queries rather than the provider brand. If the application is English-only text retrieval, a compact text model may be sufficient. If the data includes PDFs with images, Gemini Embedding 2 or Cohere embed-v4.0 may deserve a multimodal test. If the system needs Bengali, Hindi, Tamil, or transliterated queries, build language-specific evaluation slices.

For code search, use code-aware documentation or compare general models against a code-focused option. For finance or legal retrieval, domain-focused models can be candidates, but domain naming does not prove quality for a particular corpus. For privacy-sensitive data, compare hosted contracts and controls with the cost and operational burden of self-hosting Qwen3 or another open-weight model.

Indian usage also makes latency and regional availability important. Measure round-trip latency from the deployment region, tokenization behavior for local scripts, error rates, rate limits, and retry cost. Do not infer production speed from a vendor’s marketing page or a one-off local test.

For related engineering context, the AI coding cost analysis shows why usage measurement matters. Embedding cost should be tracked by model, corpus, language, and feature rather than as one monthly average.

11. How to Run a Fair Embedding Evaluation

Build a representative test set from real queries and documents. Label the relevant documents for each query, preserve the language and spelling variation that users actually produce, and separate development data from the final test set. Use the same chunking, metadata, vector database, similarity metric, top-k, and reranker for each candidate.

Measure recall at the retrieval stage before judging the generation model. Record precision, missed relevant documents, duplicate results, latency, API failures, vector size, cost, and answer support. For multilingual data, report results by language rather than only a single pooled average.

MTEB can provide a useful external reference, but it should not replace an application test. The MTEB project documentation describes evaluation across different tasks. A public score is not an assurance that a model will retrieve the right passage from your corpus.

Run the evaluation again when a provider changes the model alias, pricing, tokenizer, dimension default, or endpoint. Keep the model identifier and source date in the experiment record. A comparison article can be updated, but a production index needs a migration plan.

12. Practical Decision Checklist

Choose a starting model only after answering five questions. What content will be embedded? Which languages and modalities matter? What latency and data-location constraints apply? What vector dimension and storage budget are acceptable? How will retrieval quality be measured?

A cautious starting shortlist is OpenAI text-embedding-3-small or large for text-only API simplicity, Gemini Embedding 2 for multimodal and multilingual experiments, Cohere embed-v4.0 for mixed inputs and documented multilingual coverage, Voyage 4 for retrieval-focused controls, Jina for multilingual model testing, and Qwen3-Embedding-8B when self-hosting is justified.

That shortlist is not a ranking. It is a routing decision based on documented capabilities. The final choice should come from a reproducible evaluation with current provider prices, current model IDs, representative data, and a total-cost calculation.

Do not repeat the legacy conclusion that Gemini at $0.008 per 1M tokens is the universal value choice, that voyage-3-large is the current accuracy leader, or that an open-weight model is free to operate. Those claims were not supported by the current primary sources used here.

The AI jobs analysis and the commercial safety guide cover separate topics. For this comparison, keep the model ID, source URL, price date, benchmark task, corpus, and evaluation result in one engineering record.