[
    {
        "model_name": "NV-Embed-v2",
        "embd_dtype": "float32",
        "embd_dim": 4096,
        "num_params": 7850000000,
        "max_tokens": 32768,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/nvidia/NV-Embed-v2",
        "alias": null,
        "vendor": "NVidia",
        "tooltip": "High-performance 4096d model with 32K context, 7.8B params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "all-MiniLM-L12-v2",
        "embd_dtype": "float32",
        "embd_dim": 384,
        "num_params": 33400000,
        "max_tokens": 256,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2",
        "alias": null,
        "vendor": null,
        "tooltip": "Lightweight 384d model, fast inference, 33M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "all-MiniLM-L6-v2",
        "embd_dtype": "float32",
        "embd_dim": 384,
        "num_params": 22700000,
        "max_tokens": 256,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2",
        "alias": null,
        "vendor": null,
        "tooltip": "Ultra-compact 384d model, fastest inference, 23M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "all-mpnet-base-v2",
        "embd_dtype": "float32",
        "embd_dim": 768,
        "num_params": 109000000,
        "max_tokens": 384,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/sentence-transformers/all-mpnet-base-v2",
        "alias": null,
        "vendor": null,
        "tooltip": "Balanced 768d model, good quality-speed tradeoff",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "amazon.titan-embed-image-v1",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 128,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.aws.amazon.com/bedrock/latest/userguide/titan-multiemb-models.html",
        "alias": null,
        "vendor": "Amazon",
        "tooltip": null,
        "leaderboards": [
            "Multimodal"
        ]
    },
    {
        "model_name": "bge-base-en-v1.5",
        "embd_dtype": "float32",
        "embd_dim": 768,
        "num_params": 109000000,
        "max_tokens": 512,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-base-en-v1.5",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "Solid 768d English model, 109M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "bge-large-en-v1.5",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": 335000000,
        "max_tokens": 512,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-large-en-v1.5",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "High-quality 1024d English model, 335M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "bge-m3",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": 569000000,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-m3",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "Multilingual 1024d model with 8K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "bge-m3-retromae",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-m3-retromae",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "M3 variant with RetroMAE pretraining enhancement",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "bge-m3-unsupervised",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-m3-unsupervised",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "M3 variant trained without supervision",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "bge-small-en-v1.5",
        "embd_dtype": "float32",
        "embd_dim": 384,
        "num_params": 33400000,
        "max_tokens": 512,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/BAAI/bge-small-en-v1.5",
        "alias": null,
        "vendor": "BAAI",
        "tooltip": "Compact 384d English model, 33M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "e5-mistral-7b-instruct",
        "embd_dtype": "float32",
        "embd_dim": 4096,
        "num_params": 7110000000,
        "max_tokens": 4096,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/intfloat/e5-mistral-7b-instruct",
        "alias": null,
        "vendor": "Microsoft",
        "tooltip": "Large 4096d instruction-tuned model, 7.1B params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "embed-multilingual-v3.0",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 512,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.cohere.com/v2/docs/cohere-embed",
        "alias": null,
        "vendor": "Cohere",
        "tooltip": "Cohere's 1024d multilingual embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "embed-v4.0",
        "embd_dtype": "float32",
        "embd_dim": 1536,
        "num_params": null,
        "max_tokens": 128000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.cohere.com/v2/docs/cohere-embed",
        "alias": null,
        "vendor": "Cohere",
        "tooltip": "Cohere's latest 1536d model with 128K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "embed-v4.0",
        "embd_dtype": "binary",
        "embd_dim": 256,
        "num_params": null,
        "max_tokens": 128000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.cohere.com/v2/docs/cohere-embed",
        "alias": "embed-v4.0 (binary, 256d)",
        "vendor": "Cohere",
        "tooltip": "Cohere's latest 1536d model with 128K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "embed-v4.0",
        "embd_dtype": "int8",
        "embd_dim": 512,
        "num_params": null,
        "max_tokens": 128000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.cohere.com/v2/docs/cohere-embed",
        "alias": "embed-v4.0 (int8, 512d)",
        "vendor": "Cohere",
        "tooltip": "Cohere's latest 1536d model with 128K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "embed-v4.0-multimodal",
        "embd_dtype": "float32",
        "embd_dim": 1536,
        "num_params": null,
        "max_tokens": 128000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.cohere.com/v2/docs/cohere-embed",
        "alias": "embed-v4.0 (multimodal)",
        "vendor": "Cohere",
        "tooltip": null,
        "leaderboards": [
            "Multimodal"
        ]
    },
    {
        "model_name": "gemini-embedding-001",
        "embd_dtype": "float32",
        "embd_dim": 3072,
        "num_params": null,
        "max_tokens": 2048,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings",
        "alias": null,
        "vendor": "Google",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "gemini-embedding-2-preview",
        "embd_dtype": "float32",
        "embd_dim": 3072,
        "num_params": null,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://ai.google.dev/gemini-api/docs/embeddings",
        "alias": null,
        "vendor": "Google",
        "tooltip": null,
        "leaderboards": [
            "Text",
            "Multimodal"
        ]
    },
    {
        "model_name": "GritLM-7B",
        "embd_dtype": "float32",
        "embd_dim": 384,
        "num_params": 7240000000,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/GritLM/GritLM-7B",
        "alias": null,
        "vendor": null,
        "tooltip": "Generative retrieval model, 384d output, 7.2B params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "jina-embeddings-v2-base-en",
        "embd_dtype": "float32",
        "embd_dim": 768,
        "num_params": 137000000,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en",
        "alias": null,
        "vendor": "Jina AI",
        "tooltip": "English-focused 768d model with 8K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "jina-embeddings-v2-small-en",
        "embd_dtype": "float32",
        "embd_dim": 512,
        "num_params": 33000000,
        "max_tokens": 8192,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/jinaai/jina-embeddings-v2-small-en",
        "alias": null,
        "vendor": "Jina AI",
        "tooltip": "Compact 512d English model with 8K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "KaLM-Embedding-Gemma3-12B-2511",
        "embd_dtype": "float32",
        "embd_dim": 3840,
        "num_params": 11760000000,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Instruct: Given a query, retrieve documents that answer the query\nQuery: ",
        "corpus_instruct": null,
        "reference": "https://huggingface.co/tencent/KaLM-Embedding-Gemma3-12B-2511",
        "alias": null,
        "vendor": "Tencent",
        "tooltip": "Tencent's large 3840d model based on Gemma3-12B",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "kanon-2-embedder",
        "embd_dtype": "float32",
        "embd_dim": 1792,
        "num_params": null,
        "max_tokens": 16384,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.isaacus.com/embeddings",
        "alias": null,
        "vendor": "Isaacus",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "LaBSE",
        "embd_dtype": "float32",
        "embd_dim": 768,
        "num_params": 471000000,
        "max_tokens": 256,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/sentence-transformers/LaBSE",
        "alias": null,
        "vendor": "Google",
        "tooltip": "Google's multilingual sentence encoder, 768d",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "llama-embed-nemotron-8b",
        "embd_dtype": "float32",
        "embd_dim": 4096,
        "num_params": 7500000000,
        "max_tokens": 32768,
        "similarity": "cosine",
        "query_instruct": "Instruct: Given a query, retrieve passages that answer the query\nQuery: ",
        "corpus_instruct": null,
        "reference": "https://huggingface.co/nvidia/llama-embed-nemotron-8b",
        "alias": null,
        "vendor": "NVIDIA",
        "tooltip": "NVIDIA's instruction-aware 4096d model based on Llama-3.1-8B",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "multi-qa-MiniLM-L6-cos-v1",
        "embd_dtype": "float32",
        "embd_dim": 384,
        "num_params": 22700000,
        "max_tokens": 512,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
        "alias": null,
        "vendor": null,
        "tooltip": "QA-optimized compact model, 384d, 23M params",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "Octen-Embedding-4B",
        "embd_dtype": "float32",
        "embd_dim": 2560,
        "num_params": 4000000000,
        "max_tokens": 32768,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/Octen/Octen-Embedding-4B",
        "alias": null,
        "vendor": "Octen",
        "tooltip": "Octen's efficient 2560d model based on Qwen3, 40K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "Octen-Embedding-8B",
        "embd_dtype": "float32",
        "embd_dim": 4096,
        "num_params": 7600000000,
        "max_tokens": 32768,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/Octen/Octen-Embedding-8B",
        "alias": null,
        "vendor": "Octen",
        "tooltip": "Octen's large 4096d model based on Qwen3, 40K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "Qwen3-Embedding-8B",
        "embd_dtype": "float32",
        "embd_dim": 4096,
        "num_params": 7570000000,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://huggingface.co/Qwen/Qwen3-Embedding-8B",
        "alias": null,
        "vendor": "Alibaba",
        "tooltip": "Alibaba's large 4096d model with 32K context",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "text-embedding-004",
        "embd_dtype": "float32",
        "embd_dim": 768,
        "num_params": null,
        "max_tokens": 2048,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings",
        "alias": null,
        "vendor": "Google",
        "tooltip": "Google's latest 768d embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "text-embedding-3-large",
        "embd_dtype": "float32",
        "embd_dim": 3072,
        "num_params": null,
        "max_tokens": 8191,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://platform.openai.com/docs/guides/embeddings",
        "alias": "text-embedding-3-large (3072d)",
        "vendor": "OpenAI",
        "tooltip": "OpenAI's premium large embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "text-embedding-3-large",
        "embd_dtype": "float32",
        "embd_dim": 512,
        "num_params": null,
        "max_tokens": 8191,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://platform.openai.com/docs/guides/embeddings",
        "alias": "text-embedding-3-large (512d)",
        "vendor": "OpenAI",
        "tooltip": "OpenAI's premium large embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "text-embedding-3-small",
        "embd_dtype": "float32",
        "embd_dim": 1536,
        "num_params": null,
        "max_tokens": 8191,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://platform.openai.com/docs/guides/embeddings",
        "alias": "text-embedding-3-small (1536d)",
        "vendor": "OpenAI",
        "tooltip": "OpenAI's efficient embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "text-embedding-3-small",
        "embd_dtype": "float32",
        "embd_dim": 512,
        "num_params": null,
        "max_tokens": 8191,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://platform.openai.com/docs/guides/embeddings",
        "alias": "text-embedding-3-small (512d)",
        "vendor": "OpenAI",
        "tooltip": "OpenAI's efficient embedding model",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-3.5",
        "embd_dtype": "binary",
        "embd_dim": 256,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": "voyage-3.5 (binary, 256d)",
        "vendor": "Voyage AI",
        "tooltip": "Voyage's top model with retrieval instructions",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-3.5",
        "embd_dtype": "int8",
        "embd_dim": 512,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": "voyage-3.5 (int8, 512d)",
        "vendor": "Voyage AI",
        "tooltip": "Voyage's top model with retrieval instructions",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-3-large",
        "embd_dtype": "float32",
        "embd_dim": 2048,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-4",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-4-large",
        "embd_dtype": "float32",
        "embd_dim": 2048,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-4-lite",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-code-3",
        "embd_dtype": "float32",
        "embd_dim": 2048,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": "Represent the query for retrieving supporting documents: ",
        "corpus_instruct": "Represent the document for retrieval: ",
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": "Voyage's top model with retrieval instructions",
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-law-2",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 16000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.voyageai.com/docs/embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Text"
        ]
    },
    {
        "model_name": "voyage-multimodal-3.5",
        "embd_dtype": "float32",
        "embd_dim": 1024,
        "num_params": null,
        "max_tokens": 32000,
        "similarity": "cosine",
        "query_instruct": null,
        "corpus_instruct": null,
        "reference": "https://docs.voyageai.com/docs/multimodal-embeddings",
        "alias": null,
        "vendor": "Voyage AI",
        "tooltip": null,
        "leaderboards": [
            "Multimodal"
        ]
    }
]