Jina AI
Listed at https://jina.ai
Overall Rank #12 ⭐ Consider
❌ Proxy required (AWS US-East + EU-Frankfurt deployment) | 🌍 International
💰 Token Pricing
| Type | Price | Note |
|---|---|---|
| Input | Embeddings v3: $0.02/M tokens (10K free); Reranker v3: $0.018/M tokens (10K free); Reader: $0.02/M tokens (free up to 1M tokens); CLIP-v1: $0.02/M tokens | per million tokens |
| Output | Flat per-million-token billing, no tier-based markup; batch discount of 50% available on all endpoints | per million tokens |
💡 Free Credits:
🤖 Supported Models (9)
jina-embeddings-v3jina-embeddings-v2-base-enjina-embeddings-v2-base-dejina-embeddings-v2-base-zhjina-embeddings-v2-base-codejina-clip-v1jina-reranker-v3jina-reranker-v2-base-multilingualReader (URL → clean Markdown)
✨ Pros
- ✓jina-embeddings-v3: 570M-param SOTA open-source embedding (8K context + Matryoshka dimension reduction: 32/64/128/256/512/768/1024 switchable)
- ✓Reader API: one line of code to convert any URL to clean Markdown — the de-facto RAG data collection primitive
- ✓jina-reranker-v3: multilingual reranker supporting 100+ languages, outperforms Cohere Rerank-3 on Chinese/English mixed retrieval
- ✓CLIP-v1 multimodal embeddings: native text-image cross-modal retrieval, 89 languages
- ✓RAG-native endpoint trio: Embeddings + Reranker + Reader — can be called independently or pipelined
- ✓10K tokens/month free tier, permanent, no credit card required
- ✓Multiple open-source base models (Apache 2.0): jina-embeddings-v2-base-* 4 variants, commercial use OK
- ✓OpenAI-compatible API endpoint, near-zero migration cost
⚠️ Cons
- ×Embedding/reranker/reader only — no LLM/chat capability
- ×China access requires proxy (AWS US-East + EU-Frankfurt, no mainland edge nodes)
- ×Embeddings v3 max 8192 tokens per request; longer documents need chunking
- ×Reader API has limited support for SPA/JavaScript-rendered pages; complex web apps still need headless browser fallback
- ×No Chinese documentation, English prompts give best results
- ×No formal affiliate program (API link is just UTM source tracking)
🎯 Best For
RAG pipeline embedding + reranking + data collection; multilingual (zh/en/de/fr/es/ja/ko) retrieval; long-document chunking with dimension reduction; native LlamaIndex/LangChain/AutoGen integration