aac-pictogram-retriever-ptbr-GGUF
Papuguinho基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗DISCOVER & COMPARE
Explore AI models 2043
Tooka-SBERT
PartAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Tooka-SBERT-V2-Large
PartAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Tooka-SBERT-V2-Small
PartAI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗longformer-random-tiny
patrickvonplaten基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗wav2vec2_tiny_random
patrickvonplaten基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗tiny-random-BertModel
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗tiny-random-DebertaModel
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗tiny-random-DebertaV2Model
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗tiny-random-RobertaModel
peft-internal-testingThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗compositional-bert-large-uncased
perceptiveshawty基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-context-v1-0.6b
perplexity-ai`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*. Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately **avoid** this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-context-v1-4b
perplexity-ai`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*. Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately **avoid** this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v1-0.6b
perplexity-ai`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. - Use **`pplx-embed-v1`** for independent text embedding (queries, documents, semantic search) - Use **`pplx-embed-context-v1`** for document chunks in RAG systems where surrounding context matters > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v1-4b
perplexity-ai`pplx-embed-v1` and `pplx-embed-context-v1` are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks. - Use **`pplx-embed-v1`** for independent text embedding (queries, documents, semantic search) - Use **`pplx-embed-context-v1`** for document chunks in RAG systems where surrounding context matters > [!IMPORTANT] > `pplx-embed-v1` and `pplx-embed-context-v1` natively produce *unnormalized* int8-quantized embeddings. Ensure that you compare them via *cosine similarity*.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v1-late-0.6b
perplexity-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v2-context-9b-preview
perplexity-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v2-late-0.6b
perplexity-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗pplx-embed-v2-late-9b
perplexity-ai基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗Qwen3-Embedding-0.6B-GGUF
PeterAM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗plamo-embedding-1b
pfnetPLaMo-Embedding-1B is a Japanese text embedding model developed by Preferred Networks, Inc. It can convert Japanese text input into numerical vectors and can be used for a wide range of applications, including information retrieval, text classification, and clustering. As of early April 2025, it achieved top-class scores on JMTEB, a benchmark for Japanese text embedding. It demonstrated particularly outstanding performance, especially in retrieval tasks.
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗bge-base-financial-matryoshka
philschmid基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗xl-lexeme
pierluigic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
Input Text→ Output Embedding
Price not verifiedCheck multimodal and channel pricing separately
View details ↗No matching models
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.