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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. GPT-6 Luna ProOpenAIRemove
  2. Gemini Embedding 2GoogleRemove
  3. Perceptron Mk1.5PerceptronRemove
gpt-6-luna-pro vs gemini-embedding-2-preview vs perceptron-mk1.5
AttributeGPT-6 Luna Progpt-6-luna-proGemini Embedding 2gemini-embedding-2-previewPerceptron Mk1.5perceptron-mk1.5
Pricing
Input$0.10 / 1M$0.60 / 1M$0.15 / 1M
Output$0.50 / 1M$2.40 / 1M$1.50 / 1M
Cache Write (5m)$0.10 / 1M$0.60 / 1M$0.15 / 1M
Cache Write (1h)$0.10 / 1M$0.60 / 1M$0.15 / 1M
Cache Read$0.10 / 1M$0.60 / 1M$0.15 / 1M
Web Search$0 / 1M—$0 / 1M
Context
Max context1.1M8.2K36.9K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesNoNo
StreamingYesNoYes
Catalogue
ProviderOpenAIGooglePerceptron
Categorychatembeddingchat
Charge typePay As You GoPay As You GoPay As You Go
Released——2026-09-25
Description
SummaryGPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.