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 2.5 Flash Image (Nano Banana)gemini-2.5-flash-image | Gemini 3.7 Flashgemini-3.7-flash | DeepSeek V4.1 Flashdeepseek-v4.1-flash |
|---|---|---|---|
| Pricing | |||
| Request | $0.075 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | $0.375 / 1M | $0.30 / 1M |
| Cache Write (1h) | Not applicable | $0.375 / 1M | $0.30 / 1M |
| Cache Read | Not applicable | $0.375 / 1M | $0.30 / 1M |
| Input | — | $0.375 / 1M | $0.30 / 1M |
| Output | — | $1.88 / 1M | $1.20 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | No | Yes | Yes |
| JSON Mode | No | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | DeepSeek | ||
| Category | image | chat | chat |
| Charge type | Pay Per Request | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemini 2.5 Flash Image (“Nano Banana”) is now generally available. It’s a state-of-the-art image generation model with strong contextual understanding, supporting image creation, editing, and multi-turn conversational workflows around visuals. | 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. | DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion. |