BGE-M3
Free · Open source
Production deployments requiring permissive open-source licensing
jina-embeddings-v3
Freemium$0.018 per 1M tokens (Jina API)API
Jina's multilingual embedding model that converts text into dense vectors for semantic search and retrieval applications.
$0.018 per 1M tokens (Jina API) · Free tier: Weights downloadable under CC BY-NC 4.0 for non-commercial research and personal use only; commercial deployment requires paid Jina API or commercial license
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Screenshots 4
Pricing
- Model
- Per-token
- Price
- $0.018 per 1M tokens (Jina API)
- Free Tier
- Weights downloadable under CC BY-NC 4.0 for non-commercial research and personal use only; commercial deployment requires paid Jina API or commercial license
Summary
Jina Embeddings v3 is a text-to-vector embedding model designed to power semantic search, retrieval-augmented generation (RAG), and similarity matching across documents. It handles multilingual input and long-context documents, competing directly with OpenAI's text-embedding models and open-source alternatives like BGE. Jina offers a freemium tier with API access and paid plans scaling by token volume; exact pricing depends on usage tier but starts free for evaluation. The main trade-off: vendor lock-in through their API, plus the embedding quality lives or dies by how well it generalizes to your specific domain versus commodity alternatives.
Bottom line: *Use this when you need a managed multilingual embedding service; avoid it if you want to run embeddings locally or require maximum model transparency.*
Find your fit
Before you choose jina-embeddings-v3
More options in Embedding Models. Compare pricing, platforms, and features side by side.
Cohere Embed v4
Freemium
Production semantic search requiring multimodal support
Pros
- 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
- 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.
About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- April 12, 2026
Best For
Who it's for
- Teams building multilingual retrieval systems across 30+ languages where OpenAI and Cohere APIs aren't available or compliant.
- Self-hosted deployments where LoRA adapter limitations can be worked around and efficiency doesn't depend on optimum.
- RAG pipelines where dimension reduction via Matryoshka learning reduces vector storage cost and latency.
- Long-document retrieval tasks up to 8K tokens where late chunking preserves contextual embeddings.
What it does well
- Multilingual semantic search across Arabic, Chinese, English, French, German, Japanese, Korean, Spanish, and 20+ other languages.
- Long-document Q&A and retrieval where full documents (up to 8192 tokens) are embedded once via late chunking.
- Cost-optimized vector storage where embedding dimension is truncated from 1024 to 64–512 without significant accuracy loss.
- On-premises or VPC-bound RAG where data cannot leave the security boundary and self-hosting is required.
Compare jina-embeddings-v3
Frequently Asked Questions
- Is jina-embeddings-v3 free?
- jina-embeddings-v3 offers a free option alongside paid upgrades. Pricing: $0.018 per 1M tokens (Jina API). Check the current allowances and terms before choosing a plan.
- Does jina-embeddings-v3 have an API?
- Yes. jina-embeddings-v3 exposes a developer API. See the official documentation at https://jina.ai for details.
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Jina's multilingual embedding model that converts text into dense vectors for semantic search and retrieval applications.
Jina Embeddings v3 is a text-to-vector embedding model designed to power semantic search, retrieval-augmented generation (RAG), and similarity matching across documents. It handles multilingual input and long-context documents, competing directly with OpenAI's text-embedding models and open-source alternatives like BGE. Jina offers a freemium tier with API access and paid plans scaling by token volume; exact pricing depends on usage tier but starts free for evaluation. The main trade-off: vendor lock-in through their API, plus the embedding quality lives or dies by how well it generalizes to your specific domain versus commodity alternatives.
*Use this when you need a managed multilingual embedding service; avoid it if you want to run embeddings locally or require maximum model transparency.*
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