Jina Embeddings v4
Up to 30%Embed text at scale with Jina Embeddings v4 on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.
What is Jina Embeddings v4?
Choose Jina Embeddings v4 when your pipeline needs dense representations instead of generated prose. Jina's universal multilingual embedding model. 2048 default dimensions (truncatable to 128); 32K context. Used in dense single-vector mode. LLM.API exposes it with OpenAI-compatible embedding calls.
Providers
LLM.API routes Jina Embeddings v4 to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $20; out $0 per 1M tokens | 33K tokens | — |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Jina Embeddings v4 right here — free to start.
Suggestions for your first prompt
Code snippet
Call Jina Embeddings v4 through the OpenAI-compatible API — POST /v1/embeddings.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.embeddings.create(
model="jina-embeddings-v4",
input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding)){
"model": "jina-embeddings-v4",
"input": "Semantic search starts with strong embeddings."
}5 Core Capabilities
Batch throughput
Designed for embedding large corpora via the OpenAI-compatible API.
Low-latency lookups
Suitable for online retrieval when paired with a vector store. Tuned to how teams typically call Jina Embeddings v4.
Corpus analytics
Enables semantic exploration of knowledge bases and tickets. Tuned to how teams typically call Jina Embeddings v4.
Cross-document matching
Supports near-duplicate detection and topical grouping. Reflects Jina positioning for this endpoint.
Retrieval readiness
Fits RAG pipelines that need stable document and query embeddings. Reflects Jina positioning for this endpoint.
6 Most Valuable Use Cases
- Lead and account similarity scoring from notes with Jina Embeddings v4
- Topic clustering for content and support queues
- RAG retrieval for grounded chat answers with Jina Embeddings v4
- Semantic search over docs, tickets, and wikis
- Multilingual document matching in one index with Jina Embeddings v4
- Duplicate and near-duplicate content detection
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run Jina Embeddings v4 on LLM.API?
Unified AI Routing
Reach Jina Embeddings v4 and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale Jina Embeddings v4.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Observability
Practical: Trace prompts, tokens, and errors for Jina Embeddings v4 alongside the rest of your stack.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Practical: Swap Jina Embeddings v4 for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- Throughput and cost matter more than generative prose quality (Jina Embeddings v4)
- You are building search, RAG, or clustering on text (Jina Embeddings v4)
- You need consistent vectors from an OpenAI-compatible embeddings API (Jina Embeddings v4)
Avoid if...
- You need generative answers instead of vectors
- You only need OCR or speech features
- You require cross-model vector compatibility without re-indexing
COMMUNITY
What developers say about embedding models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Jina Embeddings v4.
- Side-by-side scoring puts OpenAI embeddings slightly ahead of Cohere overall, largely on ecosystem and community support.
- Cost comparisons favour usage-based OpenAI pricing for small and mid-sized workloads.
- Retrieval quality differences are workload-specific; reviewers recommend evaluating on your own corpus before committing.
SOURCES
Frequently Asked Questions
Is Jina Embeddings v4 a chat model?
No—Jina Embeddings v4 is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.
How is Jina Embeddings v4 priced on LLM.API?
Listed pricing metadata shows: In $0.02 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Can Jina Embeddings v4 generate paragraphs of text?
No. Jina Embeddings v4 returns embeddings (vectors). Pair it with a chat model if you need generated language.
Can I use tools or structured outputs with Jina Embeddings v4?
Tooling support varies; for pure embedding models, prefer the modalities listed rather than assuming chat tools.
Which providers serve Jina Embeddings v4?
LLM.API currently lists: jina. Availability can vary by region and account.
What is Jina Embeddings v4?
Jina's universal multilingual embedding model. 2048 default dimensions (truncatable to 128); 32K context. Used in dense single-vector mode. On LLM.API it is addressed as `jina-embeddings-v4`.
How do I call Jina Embeddings v4 via API?
Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "jina-embeddings-v4" and your LLM.API key. See the code snippet on this page.
What are limitations of Jina Embeddings v4?
Like other API models, Jina Embeddings v4 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
What is the context length for Jina Embeddings v4?
Reported context for Jina Embeddings v4 is 33K tokens. Always verify the active provider row if multiple providers are listed.
When should I choose Jina Embeddings v4?
Throughput and cost matter more than generative prose quality — especially when you specifically need Jina Embeddings v4.
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