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embed-english-v3.0 vs jina-embeddings-v3

Which one should you try?

embed-english-v3.0

Freemium

Best for English-only semantic search applications

Visit embed-english-v3.0 See full review

jina-embeddings-v3

Freemium

Best for Teams building multilingual retrieval systems across 30+ languages where OpenAI and Cohere APIs aren't available or…

Visit jina-embeddings-v3 See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

embed-english-v3.0

embed-english-v3.0

embed-english-v3.0 generates semantic embeddings from English text, producing 1,024-dimensional vectors suitable for retrieval-augmented generation, classification, clustering, and semantic search tasks. It achieves state-of-the-art performance on MTEB and BEIR benchmarks and was trained on approximately 1 billion English training pairs. The model supports batches of up to 96 inputs with 512 tokens maximum per input, and supports both text and image embedding. Pricing is $0.10 per million tokens. A notable limitation is that it requires explicit input_type specification to differentiate between search documents, queries, classification, and clustering tasks.

jina-embeddings-v3

jina-embeddings-v3

Fast multilingual embeddings that outperform OpenAI on MTEB, but LoRA adapters complicate efficient serving and newer models have widened the gap.

Attributeembed-english-v3.0jina-embeddings-v3
PricingFreemiumFreemium
Price$0.10 per million tokens$0.018 per 1M tokens (Jina API)
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceNoNo
Has APIYesYes
Self-hosted optionNoNo
PlatformsCohere API, AWS SageMaker, Azure AI Foundry, OCI Generative AI—
LanguagesEnglish (primary); text-image multimodal support—
Released2024—
Pros
  • State-of-the-art performance on MTEB and BEIR benchmarks
  • Highly cost-efficient at $0.10 per million tokens
  • Supports multimodal input (text and images) with unified embeddings
  • Batch processing up to 96 inputs per request
  • Multiple embedding output formats (float, int8, uint8, binary, base64)
  • On MTEB evaluations, achieves 65.52 average across all tasks, with particularly strong performance in classification (82.58) and sentence similarity (85.80).
  • Supports 89 languages in total, including 30 languages with the best performance across major regions.
  • Maintains 92% of retrieval performance at 64 dimensions compared to full 1024 via Matryoshka learning, enabling storage and latency savings.
  • Requires significantly less GPU memory than larger alternatives, and AWS SageMaker integration provides a streamlined path to production deployment.
  • Compared to LLM-based embeddings like e5-mistral-7b (12x larger, 4x higher output dimension), offers only 1% improvement on MTEB English while being far more cost-efficient for production.
Cons
  • English-optimized only; use embed-multilingual-v3.0 for multilingual needs
  • 512-token limit per input may truncate long documents
  • Requires explicit input_type specification for optimal results
  • The XLMRobertaLoRA architecture is incompatible with optimum, which breaks async batching libraries like infinity that rely on it for efficient serving.
  • OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.709 vs 0.674) and is 205ms faster on average, widening the performance gap at production scale.
  • The model excels in multilingual applications but may require additional evaluation for low-resource languages.
  • The API intentionally throttles throughput to manage costs; users should not expect high-volume or production-level throughput.

Made up your mind?

embed-english-v3.0

Freemium

Best for English-only semantic search applications

Visit embed-english-v3.0 See full review

jina-embeddings-v3

Freemium

Best for Teams building multilingual retrieval systems across 30+ languages where OpenAI and Cohere APIs aren't available or…

Visit jina-embeddings-v3 See full review

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