Gemini Embedding 2Google
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Perplexity DeciderPerplexity
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Related Products
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About
Gemini Embedding models, including the newer Gemini Embedding 2, are part of Google’s Gemini AI ecosystem and are designed to convert text, phrases, sentences, and code into numerical vector representations that capture their semantic meaning. Unlike generative models that produce new content, the embedding model transforms input data into dense vectors that represent meaning in a mathematical format, allowing computers to compare and analyze information based on conceptual similarity rather than exact wording. These embeddings enable applications such as semantic search, recommendation systems, document retrieval, clustering, classification, and retrieval-augmented generation pipelines. The model can process input in more than 100 languages and supports up to 2048 tokens per request, allowing it to embed longer pieces of text or code while maintaining strong contextual understanding.
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About
Perplexity Decider is a 27B decision model fine-tuned from Qwen3.8-27B for structured classification and multimodal decision tasks. Rather than generating unrestricted text, it takes an input state and a typed decision schema, then returns the selected result together with calibrated probabilities. The model supports choice decisions for selecting among predefined categories and noul decisions for estimating the probability of a yes-or-no condition. It can also accept images alongside text, allowing applications to make decisions from screenshots or other visual inputs. Example workflows include routing a support request to billing, technical support, or sales, and estimating whether a message expresses urgency. Perplexity reports an overall accuracy of 85.71% across 11 benchmarks, including FinancialPhraseBank, RAGTruth, JudgeBench, TabFact, ContractNLI, Circa, Belebele, and TruthfulQA binary.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers and data engineers who need a high-performance embedding model to convert text or code into semantic vectors for search, retrieval, and AI applications
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Audience
Teams and developers wanting to self-host structured classification, routing, and multimodal decision workflows with calibrated probabilities
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Not Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationGoogle
Founded: 1998
United States
blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-embedding-2/
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Company InformationPerplexity
Founded: 2022
United States
huggingface.co/perplexity-ai/pplx-decider-v1-27b
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
Gemini
Supported
Gemini Enterprise
Supported
Gemini Enterprise Agent Platform
Supported
Google AI Studio
Supported
Mimasa AI
Supported
Perplexity Decisions API
Not Supported
Python
Supported
Qwen3.8-27B
Not Supported
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Integrations
Gemini
Not Supported
Gemini Enterprise
Not Supported
Gemini Enterprise Agent Platform
Not Supported
Google AI Studio
Not Supported
Mimasa AI
Not Supported
Perplexity Decisions API
Supported
Python
Not Supported
Qwen3.8-27B
Supported
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