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 | Gemini 4 Argongemini-4-argon | Gemini Embedding 2gemini-embedding-2-preview |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | $0.60 / 1M |
| Output | $10.00 / 1M | $2.40 / 1M |
| Cache Write (5m) | $2.00 / 1M | $0.60 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.60 / 1M |
| Cache Read | $2.00 / 1M | $0.60 / 1M |
| Web Search | $0 / 1M | — |
| Context | ||
| Max context | 1M | 8.2K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | No |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | ||
| Category | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. | 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. |