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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. GPT-6 Sol ProOpenAIRemove
  3. Gemini Embedding 2GoogleRemove
gemini-3.5-transcribe vs gpt-6-sol-pro vs gemini-embedding-2-preview
AttributeGemini 3.5 Transcribegemini-3.5-transcribeGPT-6 Sol Progpt-6-sol-proGemini Embedding 2gemini-embedding-2-preview
Pricing
Input— Not priced per input token$2.00 / 1M$0.60 / 1M
Output— Not priced per output token$10.00 / 1M$2.40 / 1M
Cache Write (5m)Not applicable$2.00 / 1M$0.60 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$0.60 / 1M
Cache ReadNot applicable$2.00 / 1M$0.60 / 1M
Web Search$0 / 1M$0 / 1M—
Context
Max context98.3K1.1M8.2K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeNoYesNo
StreamingNoYesNo
Catalogue
ProviderGoogleOpenAIGoogle
Categoryvoicechatembedding
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.GPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost.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.