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Compare models

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

  1. Universal-3.5 ProAssemblyAIRemove
  2. Transcribe 1 ProFish AudioRemove
  3. Gemini Embedding 2GoogleRemove
universal-3-5-pro vs transcribe-1-pro vs gemini-embedding-2-preview
AttributeUniversal-3.5 Prouniversal-3-5-proTranscribe 1 Protranscribe-1-proGemini Embedding 2gemini-embedding-2-preview
Pricing
Input— Not priced per input token— Not priced per input token$0.60 / 1M
Output— Not priced per output token— Not priced per output token$2.40 / 1M
Cache Write (5m)Not applicableNot applicable$0.60 / 1M
Cache Write (1h)Not applicableNot applicable$0.60 / 1M
Cache ReadNot applicableNot applicable$0.60 / 1M
Web Search$0 / 1M$0 / 1M—
Context
Max contextN/AN/A8.2K
Max outputN/AN/AN/A
Capabilities
VisionNoNoYes
Function CallingNoNoYes
JSON ModeNoNoNo
StreamingNoNoNo
Catalogue
ProviderAssemblyAIFish AudioGoogle
Categoryvoicevoiceembedding
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-222026-09-24—
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio.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.