How much does all-MiniLM-L6-v2 cost?

all-MiniLM-L6-v2 costs $0.005/M tokens.

How much text can all-MiniLM-L6-v2 process at once?

all-MiniLM-L6-v2 accepts up to 512 tokens per input. Longer text has to be split into multiple inputs.

What inputs and outputs does all-MiniLM-L6-v2 support?

all-MiniLM-L6-v2 accepts text as input and returns embedding vectors.

Which providers serve all-MiniLM-L6-v2?

all-MiniLM-L6-v2 is served by 1 provider on OpenRouter: DeepInfra.

What other embedding models does Sentence Transformers have?

paraphrase-MiniLM-L6-v2, all-MiniLM-L12-v2, multi-qa-mpnet-base-dot-v1 and 1 more are other embedding models from Sentence Transformers.

When was all-MiniLM-L6-v2 released?

all-MiniLM-L6-v2 was released on November 17, 2025.

Sentence Transformers: all-MiniLM-L6-v2

sentence-transformers/all-minilm-l6-v2

Model weights

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.

Modalities

Price

$0.005/M tokens

Context

512

Released

Nov 17, 2025

Providers

This model is hosted by one provider. OpenRouter forwards every request without routing.

Latency


	$0.005	1.18s	99.96%

Input $0.005
1.18s latency99.96% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0.004582/M tokens

Output

$0/M tokens

all-MiniLM-L6-v2 — Price history

Price history

No effective price data for this selection

		$0.00458	$0.00	0.0%	100.0%

Effective in $0.00458Effective out $0.00
Token share
100.0%

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.96%

Availability over the last 3 days

Last 72 hours

Availability 99.96%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.93%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

WineApi

new

114Mtokens

Kilo Code

Kilo Code is an open-source AI coding agent that works across VS Code, JetBrains, and CLI to help developers ship code faster with agentic workflows.

41Mtokens

Brainfog Monitor

new

6.43Mtokens

Agent Zero

Build autonomous AI agents effortlessly.

4.96Mtokens

AI Chatbot SaaS

new

1.31Mtokens

Rest of OpenRouter

4.07M

Top 5 Apps

Not currently available through EU in-region routing. In-region routing docs

More models from sentence-transformers

paraphrase-MiniLM-L6-v2

The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval tasks.

Embeddings$0.005/M tokens

all-MiniLM-L12-v2

The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and similarity-scoring.

Embeddings$0.005/M tokens

multi-qa-mpnet-base-dot-v1

The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.

Embeddings$0.005/M tokens

all-mpnet-base-v2

The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.

Embeddings$0.005/M tokens

all-MiniLM-L6-v2

Embeddings$0.005/M tokens

Sentence Transformers: all-MiniLM-L6-v2

Providers