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.
| Attribute | GPT-6 Luna Progpt-6-luna-pro | Gemini 3.5 Transcribegemini-3.5-transcribe | Gemini Embedding 2gemini-embedding-2-preview |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | — Not priced per input token | $0.60 / 1M |
| Output | $0.50 / 1M | — Not priced per output token | $2.40 / 1M |
| Cache Write (5m) | $0.10 / 1M | Not applicable | $0.60 / 1M |
| Cache Write (1h) | $0.10 / 1M | Not applicable | $0.60 / 1M |
| Cache Read | $0.10 / 1M | Not applicable | $0.60 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1.1M | 98.3K | 8.2K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | No | No |
| Streaming | Yes | No | No |
| Catalogue | |||
| Provider | OpenAI | ||
| Category | chat | voice | embedding |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| Description | |||
| Summary | GPT-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. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | 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. |