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 3.7 Flashgemini-3.7-flash | Gemini Embedding 001gemini-embedding-001 |
|---|---|---|
| Pricing | ||
| Input | $0.375 / 1M | $0.075 / 1M |
| Output | $1.88 / 1M | $0.30 / 1M |
| Cache Write (5m) | $0.375 / 1M | $0.075 / 1M |
| Cache Write (1h) | $0.375 / 1M | $0.075 / 1M |
| Cache Read | $0.375 / 1M | $0.075 / 1M |
| Web Search | $0 / 1M | — |
| Context | ||
| Max context | 1M | 128K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | No |
| JSON Mode | Yes | No |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | ||
| Category | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning. | Gemini-Embedding-001 is Google's high-quality text embedding model designed for semantic understanding and retrieval tasks. It converts text into dense vector representations optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. The model emphasizes strong multilingual performance, high semantic accuracy, and efficient embedding generation, making it well suited for large-scale knowledge indexing and production retrieval pipelines. |