Models692 models

NVIDIA: Nemotron 3.5 Embed 1B (free)

nvidia/nemotron-3.5-embed-1b:free

Nemotron 3.5 Embed 1B is an embedding model from NVIDIA, released by the provider on 21 October 2026. It sits in the embeddings category rather than the chat or generation category, so its job is to turn inputs into vectors rather than to produce written answers. The name reflects the family it…

NVIDIA: Nemotron 3.5 Embed 8B (free)

nvidia/nemotron-3.5-embed-8b:free

NVIDIA: Nemotron 3.5 Embed 8B (free) is an embedding model from NVIDIA, published under the model id nvidia/nemotron-3.5-embed-8b:free on api14. It sits in the embeddings category, so it is built to turn inputs into vector representations rather than to generate chat replies. The provider release…

LiquidAI: LFM2.5-Embedding-350M (free)

liquid/lfm-2.5-embedding-350m:free

LiquidAI publishes LFM2.5-Embedding-350M, a text embedding model from Liquid AI that is offered on api14 under the model id liquid/lfm-2.5-embedding-350m:free. It sits in the embeddings category rather than the chat category, so it is not a conversational model and does not generate prose replies.…

VoyageAI by MongoDB: voyage-code-4

voyageai/voyage-code-4

voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It belongs to the embeddings category rather than the chat or text generation family, so its job is to turn input into vectors rather than to write replies. The provider release date is 2026-08-13. If you are building…

VoyageAI by MongoDB: voyage-multimodal-3.5

voyageai/voyage-multimodal-3.5

VoyageAI by MongoDB: voyage-multimodal-3.5 is a multimodal embedding model from Voyage AI, a company now part of MongoDB. It sits in the embeddings category rather than the chat category, so its job is to turn input content into vectors rather than to generate text responses. The model was released…

VoyageAI by MongoDB: voyage-4-lite

voyageai/voyage-4-lite

voyage-4-lite is an embedding model from Voyage AI, the company behind the Voyage family of retrieval models, now part of MongoDB. It sits in the embeddings category rather than the chat category, which means it does not generate text. Instead, it turns text into vectors that you store and compare.…

VoyageAI by MongoDB: voyage-4

voyageai/voyage-4

voyage-4 is a general-purpose embedding model from Voyage AI, the company behind the Voyage embedding family that is now part of MongoDB. It is listed on api14 under the model id voyageai/voyage-4 and sits in the embeddings category, so it is built to turn text into vectors rather than to generate…

VoyageAI by MongoDB: voyage-4-large

voyageai/voyage-4-large

VoyageAI by MongoDB: voyage-4-large is an embedding model from Voyage AI, a provider whose models are distributed under the VoyageAI by MongoDB name. It sits in the embeddings category, which means it is built to turn text into vectors rather than to generate chat responses. If you are choosing a…

NVIDIA: Nemotron 3 Embed 1B (free)

nvidia/nemotron-3-embed-1b:free

NVIDIA Nemotron 3 Embed 1B is an open text embedding model from NVIDIA. It belongs to the Nemotron 3 family and is distributed as a free model on api14 under the identifier nvidia/nemotron-3-embed-1b:free. NVIDIA positions it as an embedding model rather than a generative chat model, so its job is…

Google: Gemini Embedding 2

google/gemini-embedding-2

Google: Gemini Embedding 2 is an embedding model from Google, listed on api14 under the model id google/gemini-embedding-2. It sits in the embeddings category and is described by Google as the company's first multimodal embedding model. Rather than generating chat responses, it converts inputs into…

Google: Gemini Embedding 2 (batch)

google/gemini-embedding-2:batch

Google: Gemini Embedding 2 (batch) is an embedding model from Google. It is listed on api14 under the embeddings category, and its provider release date is 2026-05-20. Google describes it as Gemini Embedding 2, the company's first multimodal embedding model. Rather than generating chat replies, it…

Google: Gemini Embedding 2 Preview

google/gemini-embedding-2-preview

Google: Gemini Embedding 2 Preview is an embedding model from Google, published under the model id google/gemini-embedding-2-preview. It sits in the embeddings category and is described by Google as its first multimodal embedding model. It is a preview release, which is worth noting if you are…

Perplexity: Embed V1 4B

perplexity/pplx-embed-v1-4b

Perplexity: Embed V1 4B is a text embedding model from Perplexity, released by the provider on 16 March 2026. It belongs to the embeddings category rather than the chat category, which means it does not generate conversational answers. Instead it turns text into vector representations that you…

Perplexity: Embed V1 0.6B

perplexity/pplx-embed-v1-0.6b

Perplexity: Embed V1 0.6B is a text embedding model from Perplexity, released on 16 March 2026. It belongs to the pplx-embed-v1 family, which Perplexity describes as state of the art text embedding models built for real-world, web-scale retrieval. The 0.6B parameter variant targets lightweight,…

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

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

Llama Nemotron Embed VL 1B V2 is an embedding model from NVIDIA, distributed on api14 under the model id nvidia/llama-nemotron-embed-vl-1b-v2:free. It belongs to the embeddings category, and its stated purpose is multimodal question-answering retrieval. It is a 1B class model, which places it among…

Thenlper: GTE-Base

thenlper/gte-base

Thenlper: GTE-Base is an embedding model from Thenlper, listed on api14 under the model id thenlper/gte-base. It sits in the embeddings category, so it is not a chat or text generation model. Its job is to turn text into vectors that represent meaning, and the provider describes it as encoding…

Thenlper: GTE-Large

thenlper/gte-large

Thenlper: GTE-Large is an embedding model from Thenlper, listed on api14 under the model id thenlper/gte-large. It sits in the embeddings category, which means its job is not to generate chat replies but to turn text into vectors that you can store, compare and search over. If your application…

Intfloat: E5-Large-v2

intfloat/e5-large-v2

Intfloat: E5-Large-v2 is an embeddings model from Intfloat. It belongs to the embeddings category, which means its job is to turn text into vectors rather than to generate chat responses. The provider describes it as mapping English sentences, paragraphs, and documents into a 1024-dimensional dense…

Intfloat: E5-Base-v2

intfloat/e5-base-v2

Intfloat: E5-Base-v2 is an embedding model from Intfloat, listed on api14 under the model id intfloat/e5-base-v2. It sits in the embeddings category, which means it is not a chat or text generation model. Instead of producing replies, it turns text into vectors that you can store, compare and…

Intfloat: Multilingual-E5-Large

intfloat/multilingual-e5-large

Intfloat: Multilingual-E5-Large is an embedding model from Intfloat, listed on api14 under the embeddings category. It takes text as input and returns embeddings as output, so it is not a chat or text generation model. Its purpose is to turn language into vectors that you can store, compare and…

Sentence Transformers: paraphrase-MiniLM-L6-v2

sentence-transformers/paraphrase-minilm-l6-v2

Sentence Transformers: paraphrase-MiniLM-L6-v2 is an embedding model from Sentence Transformers, listed on api14 under the embeddings category. It is a compact MiniLM-based model whose purpose is to turn text into vectors rather than to generate text. The provider release date recorded for it is…

Sentence Transformers: all-MiniLM-L12-v2

sentence-transformers/all-minilm-l12-v2

Sentence Transformers: all-MiniLM-L12-v2 is an embedding model from Sentence Transformers, distributed through api14 under the model id sentence-transformers/all-minilm-l12-v2. It belongs to the embeddings category, so its job is not to generate chat replies but to turn text into vectors that you…

BAAI: bge-base-en-v1.5

baai/bge-base-en-v1.5

BAAI: bge-base-en-v1.5 is an embedding model from BAAI, the Beijing Academy of Artificial Intelligence. It belongs to the embeddings category, which means it is not built to generate chat replies or complete text. Instead, it turns text into numerical vectors. It is an English-language model, and…

Sentence Transformers: multi-qa-mpnet-base-dot-v1

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

Sentence Transformers: multi-qa-mpnet-base-dot-v1 is an embedding model from Sentence Transformers, a provider known for its sentence-level representation models. It sits in the embeddings category, which means it does not generate text. Instead, it converts text into vectors that you can store,…

BAAI: bge-large-en-v1.5

baai/bge-large-en-v1.5

BAAI bge-large-en-v1.5 is an embedding model from BAAI, the Beijing Academy of Artificial Intelligence. It sits in the embeddings category rather than the chat category, which means it does not generate conversational text. Instead, it turns text into vectors that you can store, compare, and search…

BAAI: bge-m3

baai/bge-m3

BAAI: bge-m3 is an embedding model from BAAI, the Beijing Academy of Artificial Intelligence. It belongs to the embeddings category, which means it is built to turn text into vectors rather than to generate chat responses. If your application needs a model that answers questions or writes text,…

Sentence Transformers: all-mpnet-base-v2

sentence-transformers/all-mpnet-base-v2

Sentence Transformers: all-mpnet-base-v2 is an embedding model from Sentence Transformers, a family of models built for turning text into vectors. It sits in the embeddings category, meaning it is designed to produce representations of text rather than to generate text itself. The model encodes…

Sentence Transformers: all-MiniLM-L6-v2

sentence-transformers/all-minilm-l6-v2

Sentence Transformers: all-MiniLM-L6-v2 is an embedding model from Sentence Transformers, listed on api14 under the embeddings category. It is a compact sentence embedding model rather than a chat or text generation model, so its job is to turn text into vectors that capture meaning. If you are…

Mistral: Mistral Embed 2312

mistralai/mistral-embed-2312

Mistral Embed 2312 is an embedding model from Mistral, listed on api14 under the model id mistralai/mistral-embed-2312. It sits in the embeddings category, which means its job is to turn text into vectors rather than to generate chat replies. Mistral AI developed it as a specialized model for text…

Google: Gemini Embedding 001

google/gemini-embedding-001

Google: Gemini Embedding 001 is an embedding model from Google, listed on api14 under the model id google/gemini-embedding-001. It sits in the embeddings category, and Google released it on 31 October 2025. Its purpose is to turn text into vector representations that you can store, compare and…

OpenAI: Text Embedding Ada 002

openai/text-embedding-ada-002

OpenAI: Text Embedding Ada 002 is an embeddings model from OpenAI, listed on api14 under the model id openai/text-embedding-ada-002. It belongs to the embeddings category and is described by the provider as OpenAI's legacy text embedding model. If you have worked with OpenAI's embedding endpoints…

OpenAI: Text Embedding Ada 002 (batch)

openai/text-embedding-ada-002:batch

OpenAI: Text Embedding Ada 002 (batch) is an embeddings model from OpenAI, distributed through api14 under the model id openai/text-embedding-ada-002:batch. It belongs to the embeddings category, and OpenAI describes text-embedding-ada-002 as its legacy text embedding model. If you have worked with…

Mistral: Codestral Embed 2505

mistralai/codestral-embed-2505

Mistral Codestral Embed 2505 is an embeddings model from Mistral, released on 30 October 2025 and published on api14 under the model id mistralai/codestral-embed-2505. It sits in the embeddings category rather than the chat category, which means it is not built to hold a conversation or generate…

OpenAI: Text Embedding 3 Large

openai/text-embedding-3-large

OpenAI: Text Embedding 3 Large is an embedding model from OpenAI, listed on api14 under the model id openai/text-embedding-3-large. It sits in the embeddings category rather than the chat category, which means it is built to turn text into vectors rather than to generate conversational replies.…

OpenAI: Text Embedding 3 Large (batch)

openai/text-embedding-3-large:batch

OpenAI: Text Embedding 3 Large (batch) is an embedding model from OpenAI. It sits in the embeddings category, and OpenAI describes text-embedding-3-large as its most capable embedding model for both English and non-English tasks. The batch variant exposed on api14 uses the model id…

OpenAI: Text Embedding 3 Small

openai/text-embedding-3-small

OpenAI: Text Embedding 3 Small is an embedding model from OpenAI, listed on api14 under the model id openai/text-embedding-3-small. It sits in the embeddings category, and OpenAI describes it as an improved, more performant version of the ada embedding model. Rather than generating chat replies, it…

OpenAI: Text Embedding 3 Small (batch)

openai/text-embedding-3-small:batch

OpenAI: Text Embedding 3 Small (batch) is an embedding model from OpenAI, offered through api14. It belongs to the embeddings category, and its model id on api14 is openai/text-embedding-3-small:batch. OpenAI describes text-embedding-3-small as an improved, more performant version of the ada…

Qwen: Qwen3 Embedding 8B

qwen/qwen3-embedding-8b

Qwen3 Embedding 8B is a text embedding model from Qwen, part of the Qwen3 Embedding series that Qwen describes as its latest proprietary line built specifically for text embedding and ranking tasks. It sits in the embeddings category rather than the chat category, so its job is to turn text into…

Qwen: Qwen3 Embedding 4B

qwen/qwen3-embedding-4b

Qwen: Qwen3 Embedding 4B is an embedding model from Qwen, part of the Qwen3 Embedding series. The series is described as Qwen's latest proprietary model family built specifically for text embedding and ranking tasks, and it inherits the multilingual capabilities and long-text handling of the wider…