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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Perceptron Mk1.5PerceptronRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. Gemini Embedding 2GoogleRemove
perceptron-mk1.5 vs gemini-3.5-transcribe vs gemini-embedding-2-preview
AttributePerceptron Mk1.5perceptron-mk1.5Gemini 3.5 Transcribegemini-3.5-transcribeGemini Embedding 2gemini-embedding-2-preview
Pricing
Input$0.15 / 1M— Not priced per input token$0.60 / 1M
Output$1.50 / 1M— Not priced per output token$2.40 / 1M
Cache Write (5m)$0.15 / 1MNot applicable$0.60 / 1M
Cache Write (1h)$0.15 / 1MNot applicable$0.60 / 1M
Cache Read$0.15 / 1MNot applicable$0.60 / 1M
Web Search$0 / 1M$0 / 1M—
Context
Max context36.9K98.3K8.2K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoNoNo
StreamingYesNoNo
Catalogue
ProviderPerceptronGoogleGoogle
Categorychatvoiceembedding
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-252026-09-25—
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.