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 | GPT-6 Astra Progpt-6-astra-pro | Gemini Embedding 001gemini-embedding-001 |
|---|---|---|---|
| Pricing | |||
| Input | $0.375 / 1M | $10.00 / 1M | $0.075 / 1M |
| Output | $1.88 / 1M | $50.00 / 1M | $0.30 / 1M |
| Cache Write (5m) | $0.375 / 1M | $10.00 / 1M | $0.075 / 1M |
| Cache Write (1h) | $0.375 / 1M | $10.00 / 1M | $0.075 / 1M |
| Cache Read | $0.375 / 1M | $10.00 / 1M | $0.075 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1M | 1M | 128K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | ||
| Category | chat | chat | embedding |
| Charge type | Pay As You Go | 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. | GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost. | 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. |