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.

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.

Which endpoint do I call for Jina Embeddings v4?

POST https://api.llmapi.ai/v1/chat/completions with model "jina-embeddings-v4" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Jina Embeddings v4 on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Jina Embeddings v4 support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

Jina Embeddings v4: Self-Hosting & Deployment Guide

Embed text at scale with Jina Embeddings v4 on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.

Start Using 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.

How to run Jina Embeddings v4 yourself

Open weights, 3.8B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Jina Embeddings v4 VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	7.5 GB	Estimate	3.8B params × 2 bytes
GGUF Q8_0	3.3 GB	File size	jinaai/jina-embeddings-v4-text-retrieval-GGUF
GGUF Q4_K_M	1.9 GB	File size	jinaai/jina-embeddings-v4-text-retrieval-GGUF
GGUF Q2_K	1.3 GB	File size	jinaai/jina-embeddings-v4-text-retrieval-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy Jina Embeddings v4 on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
NVIDIA H200141 GB	1×	1×	1×	1×
NVIDIA H10080 GB	1×	1×	1×	1×
NVIDIA A100 80GB80 GB	1×	1×	1×	1×
NVIDIA L40S48 GB	1×	1×	1×	1×

GPUs needed per variant.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
RTX 509032 GB	1×	1×	1×
RTX 409024 GB	1×	1×	1×
RTX 309024 GB	1×	1×	1×

GGUF via llama.cpp; more cards or CPU offload for larger files.

Hardware	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
Mac, 512 GBM3 Ultra	Fits	Fits	Fits
Mac, 192 GBM2 Ultra	Fits	Fits	Fits
Mac, 128 GBM4 Max / M3 Max	Fits	Fits	Fits
Mac, 64 GBM4 Pro / Max	Fits	Fits	Fits

Assumes ~75% of unified memory is usable by the GPU.

Hardware	BF16 (full precision)	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	Fits	Fits	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	Fits	Fits	Fits

Whole-instance GPU memory; public instance specs.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

LLM.API routes Jina Embeddings v4 to the providers below, with discounted effective rates versus list price.

List price by provider

InputOutput

jina$0.02 in

$0 out

Provider list prices; the LLM.API discount applies on top.

Provider	Pricing	Context	Capabilities
jina	in $0.02; out $0 per 1M tokens	33K tokens	—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test Jina Embeddings v4 right here — free to start.

Jina Embeddings v4

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Jina Embeddings v4 through the OpenAI-compatible API — POST /v1/embeddings.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to jina-embeddings-v4 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "jina-embeddings-v4",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="jina-embeddings-v4",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "jina-embeddings-v4",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="jina-embeddings-v4",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "jina-embeddings-v4",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID jina-embeddings-v4 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Not supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Jina Embeddings v4 in production

Best workloadsLLM.API features

What the model is good at.

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.

What you get when you call Jina Embeddings v4 through LLM.API.

Unified AI Routing
Reach Jina Embeddings v4 and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Jina Embeddings v4.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Practical: Trace prompts, tokens, and errors for Jina Embeddings v4 alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Practical: Swap Jina Embeddings v4 for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

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

LLM API rating for Jina Embeddings v4

★ 2.6/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

2.5Production adoptionNot yet tracked — no public usage data for this release

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

2.6Developer ecosystemContext window, tool and JSON support, price against the category median

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about embedding models

LMArena—Speed—Sources: LMArena, Artificial Analysis

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.

COMPARE

Competitive Models

Get one key to every model

Swap your API key. Keep your code.

Start for free Talk to sales

LLM API sells to AI agents - live prices and self-serve payment

Jina Embeddings v4: Self-Hosting & Deployment Guide

What is Jina Embeddings v4?

How to run Jina Embeddings v4 yourself

Jina Embeddings v4 VRAM requirements

GPU requirements: deploy Jina Embeddings v4 on your hardware

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Jina Embeddings v4 in production

Batch throughput

Low-latency lookups

Corpus analytics

Cross-document matching

Retrieval readiness

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth