Can Jina Embeddings v5 Text Small generate paragraphs of text?

No. Jina Embeddings v5 Text Small returns embeddings (vectors). Pair it with a chat model if you need generated language.

Where is the canonical page for Jina Embeddings v5 Text Small?

https://llmapi.ai/models/jina-embeddings-v5-text-small/

Is Jina Embeddings v5 Text Small a chat model?

No—Jina Embeddings v5 Text Small is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.

When should I choose Jina Embeddings v5 Text Small?

You need consistent vectors from an OpenAI-compatible embeddings API — especially when you specifically need Jina Embeddings v5 Text Small.

What are limitations of Jina Embeddings v5 Text Small?

Like other API models, Jina Embeddings v5 Text Small can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

How is Jina Embeddings v5 Text Small 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.

Which providers serve Jina Embeddings v5 Text Small?

LLM.API currently lists: jina. Availability can vary by region and account.

What is the context length for Jina Embeddings v5 Text Small?

Reported context for Jina Embeddings v5 Text Small is 33K tokens. Always verify the active provider row if multiple providers are listed.

Which endpoint do I call for Jina Embeddings v5 Text Small?

POST https://api.llmapi.ai/v1/chat/completions with model "jina-embeddings-v5-text-small" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Jina Embeddings v5 Text Small 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 v5 Text Small support function calling and structured outputs?

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

Jina Embeddings v5 Text Small: Self-Hosting & Deployment Guide

Jina Embeddings v5 Text Small delivers Jina embeddings through LLM.API so retrieval stacks stay simple and portable.

Start Using API

What is Jina Embeddings v5 Text Small?

Jina Embeddings v5 Text Small is an embedding model on LLM.API (`jina-embeddings-v5-text-small`). Jina's SOTA small multilingual text embedding model. 1024 default dimensions (Matryoshka, truncatable to 32); 32K context. It maps text into embedding vectors for retrieval, clustering, and semantic search—not for chat completions.

How to run Jina Embeddings v5 Text Small yourself

Open weights (cc-by-nc-4.0), 596M parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

Jina Embeddings v5 Text Small VRAM requirements

Precision / quantization	Weights	Figure	Source
BF16 (full precision)	1.2 GB	Estimate	596M params × 2 bytes
MLX 4-bit	0.34 GB	File size	aigentic/jina-embeddings-v5-text-small-classification-mlx-mlx-4Bit
GGUF Q8_0	0.64 GB	File size	jinaai/jina-embeddings-v5-text-small-retrieval-GGUF
GGUF Q4_K_M	0.4 GB	File size	jinaai/jina-embeddings-v5-text-small-retrieval-GGUF
GGUF Q2_K	0.3 GB	File size	jinaai/jina-embeddings-v5-text-small-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 v5 Text Small on your hardware

Datacenter GPUsConsumer GPUsApple SiliconCloud instances

Hardware	BF16 (full precision)	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
NVIDIA H200141 GB	1×	1×	1×	1×	1×
NVIDIA H10080 GB	1×	1×	1×	1×	1×
NVIDIA A100 80GB80 GB	1×	1×	1×	1×	1×
NVIDIA L40S48 GB	1×	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	MLX 4-bit
Mac, 512 GBM3 Ultra	Fits	Fits	Fits	Fits
Mac, 192 GBM2 Ultra	Fits	Fits	Fits	Fits
Mac, 128 GBM4 Max / M3 Max	Fits	Fits	Fits	Fits
Mac, 64 GBM4 Pro / Max	Fits	Fits	Fits	Fits

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

Hardware	BF16 (full precision)	MLX 4-bit	GGUF Q8_0	GGUF Q4_K_M	GGUF Q2_K
AWS p5.48xlarge8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits
AWS p4de.24xlarge8× A100 80GB · 640 GB	Fits	Fits	Fits	Fits	Fits
AWS g6e.12xlarge4× L40S · 192 GB	Fits	Fits	Fits	Fits	Fits
Google Cloud a3-highgpu-8g8× H100 · 640 GB	Fits	Fits	Fits	Fits	Fits
Azure ND H100 v58× H100 · 640 GB	Fits	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, with custom volume discounts on request.

Get API key See API code ↓

Providers

LLM.API routes Jina Embeddings v5 Text Small 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 11 Oct 2026.

Try this model

Test Jina Embeddings v5 Text Small right here — free to start.

Jina Embeddings v5 Text Small

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Jina Embeddings v5 Text Small 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-v5-text-small 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-v5-text-small",
    "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-v5-text-small",
    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-v5-text-small",
  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-v5-text-small",
    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-v5-text-small",
  "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-v5-text-small · 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 v5 Text Small in production

Best workloadsLLM.API features

What the model is good at.

Classification features
Vectors can feed lightweight classifiers and routing logic.
Cross-document matching
Supports near-duplicate detection and topical grouping. Reflects Jina positioning for this endpoint.
Semantic vectorization
Maps text into dense vectors for similarity search and clustering. Grounded in the model's embedding role rather than generic chat claims.
Batch throughput
Designed for embedding large corpora via the OpenAI-compatible API. Relevant for `jina-embeddings-v5-text-small` workloads on LLM.API.
Multilingual text
Embeds content across languages depending on the underlying model family. Reflects Jina positioning for this endpoint.

What you get when you call Jina Embeddings v5 Text Small through LLM.API.

Unified AI Routing
Reach Jina Embeddings v5 Text Small and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Production: Compare provider price points and keep spend visible as you scale Jina Embeddings v5 Text Small.
More performance, less spend.
Reliability Layer
Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for Jina Embeddings v5 Text Small 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
Production: Swap Jina Embeddings v5 Text Small for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

When to Use — When NOT to Use

Use it if...

You need consistent vectors from an OpenAI-compatible embeddings API (Jina Embeddings v5 Text Small)
Throughput and cost matter more than generative prose quality (Jina Embeddings v5 Text Small)
You are building search, RAG, or clustering on text (Jina Embeddings v5 Text Small)

Avoid if...

You only need OCR or speech features
You require cross-model vector compatibility without re-indexing
You need generative answers instead of vectors

LLM API rating for Jina Embeddings v5 Text Small

★ 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 v5 Text Small.

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

Can Jina Embeddings v5 Text Small generate paragraphs of text?
No. Jina Embeddings v5 Text Small returns embeddings (vectors). Pair it with a chat model if you need generated language.
Where is the canonical page for Jina Embeddings v5 Text Small?
https://llmapi.ai/models/jina-embeddings-v5-text-small/
Is Jina Embeddings v5 Text Small a chat model?
No—Jina Embeddings v5 Text Small is categorized as a embedding model. Use the matching API surface rather than assuming chat completions.
When should I choose Jina Embeddings v5 Text Small?
You need consistent vectors from an OpenAI-compatible embeddings API — especially when you specifically need Jina Embeddings v5 Text Small.
What are limitations of Jina Embeddings v5 Text Small?
Like other API models, Jina Embeddings v5 Text Small can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How is Jina Embeddings v5 Text Small 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.
What is Jina Embeddings v5 Text Small?
Jina's SOTA small multilingual text embedding model. 1024 default dimensions (Matryoshka, truncatable to 32); 32K context. On LLM.API it is addressed as `jina-embeddings-v5-text-small`.
Which providers serve Jina Embeddings v5 Text Small?
LLM.API currently lists: jina. Availability can vary by region and account.
What is the context length for Jina Embeddings v5 Text Small?
Reported context for Jina Embeddings v5 Text Small is 33K tokens. Always verify the active provider row if multiple providers are listed.

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Jina Embeddings v5 Text Small: Self-Hosting & Deployment Guide

What is Jina Embeddings v5 Text Small?

How to run Jina Embeddings v5 Text Small yourself

Jina Embeddings v5 Text Small VRAM requirements

GPU requirements: deploy Jina Embeddings v5 Text Small on your hardware

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Jina Embeddings v5 Text Small in production

Classification features

Cross-document matching

Semantic vectorization

Batch throughput

Multilingual text

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth