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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.

GPT-6 LunaOpenAI Remove
Gemini Embedding 2Google Remove
Universal-3.5 ProAssemblyAI Remove

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gpt-6-luna vs gemini-embedding-2-preview vs universal-3-5-pro
Attribute	GPT-6 Lunagpt-6-luna	Gemini Embedding 2gemini-embedding-2-preview	Universal-3.5 Prouniversal-3-5-pro
Pricing
Input	$0.10 / 1M	$0.60 / 1M	— Not priced per input token
Output	$0.50 / 1M	$2.40 / 1M	— Not priced per output token
Cache Write (5m)	$0.10 / 1M	$0.60 / 1M	Not applicable
Cache Write (1h)	$0.10 / 1M	$0.60 / 1M	Not applicable
Cache Read	$0.10 / 1M	$0.60 / 1M	Not applicable
Web Search	$0 / 1M	—	$0 / 1M
Context
Max context	1.1M	8.2K	N/A
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No
Function Calling	Yes	Yes	No
JSON Mode	Yes	No	No
Streaming	Yes	No	No
Catalogue
Provider	OpenAI	Google	AssemblyAI
Category	chat	embedding	voice
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	2026-09-22
Description
Summary	GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability.	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.	AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.