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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. Gemini 3.5 TranscribeGoogleRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Gemini Embedding 2GoogleRemove
gemini-3.5-transcribe vs perceptron-mk1.5 vs gemini-embedding-2-preview
AttributeGemini 3.5 Transcribegemini-3.5-transcribePerceptron Mk1.5perceptron-mk1.5Gemini Embedding 2gemini-embedding-2-preview
Pricing
Input— Not priced per input token$0.15 / 1M$0.60 / 1M
Output— Not priced per output token$1.50 / 1M$2.40 / 1M
Cache Write (5m)Not applicable$0.15 / 1M$0.60 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$0.60 / 1M
Cache ReadNot applicable$0.15 / 1M$0.60 / 1M
Web Search$0 / 1M$0 / 1M—
Context
Max context98.3K36.9K8.2K
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeNoNoNo
StreamingNoYesNo
Catalogue
ProviderGooglePerceptronGoogle
Categoryvoicechatembedding
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-252026-09-25—
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per 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.