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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. Gemini Embedding 2GoogleRemove
  3. GPT-6.1 SolOpenAIRemove
universal-3-5-pro vs gemini-embedding-2-preview vs gpt-6.1-sol
AttributeUniversal-3.5 Prouniversal-3-5-proGemini Embedding 2gemini-embedding-2-previewGPT-6.1 Solgpt-6.1-sol
Pricing
Input— Not priced per input token$0.60 / 1M$2.00 / 1M
Output— Not priced per output token$2.40 / 1M$10.00 / 1M
Cache Write (5m)Not applicable$0.60 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$0.60 / 1M$2.00 / 1M
Cache ReadNot applicable$0.60 / 1M$2.00 / 1M
Web Search$0 / 1M—$0 / 1M
Context
Max contextN/A8.2K1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoNoYes
StreamingNoNoYes
Catalogue
ProviderAssemblyAIGoogleOpenAI
Categoryvoiceembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-22——
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. 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.GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.