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Models

Explore the active model market,from a local OpenRouter snapshot.

This page reads from a local JSON snapshot synced from OpenRouter, so the catalog stays fast, indexable, and stable. Use it to browse current model coverage by provider, modality, reasoning support, context window, and pricing metadata.

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Results

Showing 26 of 26 matching models

Snapshot source: OpenRouter. Synced April 21, 2026 at 8:00 AM. Page 1 of 1.

This route is built from local JSON so the catalog stays stable for browsing and SEO. If you need a specific model on ImaRouter, treat this page as a discovery reference and then contact the team for availability.

Embeddings

Google AI Studio

Google: Gemini Embedding 2 Preview

Gemini Embedding 2 Preview is Google's first multimodal embedding model, mapping text, images, video, audio, and PDFs into a unified vector space for semantic search and retrieval-augmented generation (RAG). It supports input context up to 8,192 tokens and flexible output dimensions from 128 to 3,072 (recommended: 768, 1536, or 3,072). Designed for cross-modal similarity β€” you can embed a text query and retrieve the most relevant images, or vice versa β€” making it well-suited for multimodal search, recommendation, and document understanding pipelines.

EmbeddingsTextImage

Context

8.2K

Group

Gemini

Pricing preview

Text Input: $0.2 /M tokens

Image Input: $0.45 /M tokens

Slug

google/gemini-embedding-2-preview

Embeddings

OpenAI

OpenAI: Text Embedding Ada 002

text-embedding-ada-002 is OpenAI's legacy text embedding model.

EmbeddingsText

Context

8.2K

Group

Other

Pricing preview

Input Price: $0.1 /M tokens

Slug

openai/text-embedding-ada-002

Embeddings

OpenAI

OpenAI: Text Embedding 3 Large

text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

EmbeddingsText

Context

8.2K

Group

Other

Pricing preview

Input Price: $0.13 /M tokens

Slug

openai/text-embedding-3-large

Embeddings

OpenAI

OpenAI: Text Embedding 3 Small

text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

EmbeddingsText

Context

8.2K

Group

Other

Pricing preview

Input Price: $0.02 /M tokens

Slug

openai/text-embedding-3-small

Embeddings

Perplexity

Perplexity: Embed V1 0.6B

pplx-embed-v1-0.6B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 0.6B parameter model targeting lightweight, low-latency embedding generation.

EmbeddingsText

Context

32K

Group

Other

Pricing preview

Input Price: $0.004 /M tokens

Slug

perplexity/pplx-embed-v1-0.6b

Embeddings

Perplexity

Perplexity: Embed V1 4B

pplx-embed-v1 -4B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 4B parameter model maximizing retrieval quality.

EmbeddingsText

Context

32K

Group

Other

Pricing preview

Input Price: $0.03 /M tokens

Slug

perplexity/pplx-embed-v1-4b

Embeddings

NVIDIA

NVIDIA: Llama Nemotron Embed VL 1B V2 (free)

The Llama Nemotron Embed VL 1B V2 embedding model is optimized for multimodal question-answering retrieval. The model can embed 'documents' in the form of image, text, or image and text combined. Documents can be retrieved given a user query in text form. The model supports images containing text, tables, charts, and infographics.

EmbeddingsTextImage

Context

131.1K

Group

Other

Pricing preview

Input Price: $0 /M tokens

Output Price: $0 /M tokens

Slug

nvidia/llama-nemotron-embed-vl-1b-v2

Embeddings

DeepInfra

Intfloat: Multilingual-E5-Large

The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and large-scale data search.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

intfloat/multilingual-e5-large

Embeddings

DeepInfra

Intfloat: E5-Base-v2

The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring, retrieval and clustering.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

intfloat/e5-base-v2

Embeddings

DeepInfra

Intfloat: E5-Large-v2

The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

intfloat/e5-large-v2

Embeddings

DeepInfra

Thenlper: GTE-Large

The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

thenlper/gte-large

Embeddings

DeepInfra

Thenlper: GTE-Base

The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

thenlper/gte-base

Embeddings

DeepInfra

BAAI: bge-m3

The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.

EmbeddingsText

Context

8.2K

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

baai/bge-m3

Embeddings

DeepInfra

BAAI: bge-large-en-v1.5

The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks in English.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

baai/bge-large-en-v1.5

Embeddings

DeepInfra

Sentence Transformers: 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.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

sentence-transformers/multi-qa-mpnet-base-dot-v1

Embeddings

DeepInfra

BAAI: bge-base-en-v1.5

The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features improved similarity-score distribution and stronger retrieval performance out of the box.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

baai/bge-base-en-v1.5

Embeddings

DeepInfra

Sentence Transformers: 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.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

sentence-transformers/all-minilm-l12-v2

Embeddings

DeepInfra

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.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

sentence-transformers/paraphrase-minilm-l6-v2

Embeddings

DeepInfra

Sentence Transformers: all-MiniLM-L6-v2

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.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

sentence-transformers/all-minilm-l6-v2

Embeddings

DeepInfra

Sentence Transformers: 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.

EmbeddingsText

Context

512

Group

Other

Pricing preview

Input Price: $0.005 /M tokens

Slug

sentence-transformers/all-mpnet-base-v2

Embeddings

Unknown provider

Qwen: Qwen3 Embedding 0.6B

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

EmbeddingsText

Context

8.2K

Group

Other

Pricing preview

No display pricing published in the current snapshot.

Slug

qwen/qwen3-embedding-0.6b

Embeddings

Mistral

Mistral: Mistral Embed 2312

Mistral Embed is a specialized embedding model for text data, optimized for semantic search and RAG applications. Developed by Mistral AI in late 2023, it produces 1024-dimensional vectors that effectively capture semantic relationships in text.

EmbeddingsText

Context

8.2K

Group

Mistral

Pricing preview

Input Price: $0.1 /M tokens

Slug

mistralai/mistral-embed-2312

Embeddings

Google AI Studio

Google: Gemini Embedding 001

gemini-embedding-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard since the experimental launch in March.

EmbeddingsText

Context

20K

Group

Gemini

Pricing preview

Input Price: $0.15 /M tokens

Slug

google/gemini-embedding-001

Embeddings

Mistral

Mistral: Codestral Embed 2505

Mistral Codestral Embed is specially designed for code, perfect for embedding code databases, repositories, and powering coding assistants with state-of-the-art retrieval.

EmbeddingsText

Context

8.2K

Group

Mistral

Pricing preview

Input Price: $0.15 /M tokens

Slug

mistralai/codestral-embed-2505

Embeddings

Nebius Token Factory

Qwen: Qwen3 Embedding 8B

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

EmbeddingsText

Context

32K

Group

Other

Pricing preview

Input Price: $0.01 /M tokens

Slug

qwen/qwen3-embedding-8b

Embeddings

DeepInfra

Qwen: Qwen3 Embedding 4B

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

EmbeddingsText

Context

32.8K

Group

Other

Pricing preview

Input Price: $0.02 /M tokens

Slug

qwen/qwen3-embedding-4b

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