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 3.8 Flashgemini-3.8-flash | DeepSeek V4 Flashdeepseek-v4-flash |
|---|---|---|---|
| Pricing | |||
| Input | $0.375 / 1M | $0.75 / 1M | $0.14 / 1M |
| Output | $1.88 / 1M | $3.75 / 1M | $0.28 / 1M |
| Cache Write (5m) | $0.375 / 1M | $0.75 / 1M | $0.14 / 1M |
| Cache Write (1h) | $0.375 / 1M | $0.75 / 1M | $0.14 / 1M |
| Cache Read | $0.375 / 1M | $0.75 / 1M | $0.14 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 1.0M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | DeepSeek | ||
| Category | chat | chat | chat |
| 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. | Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model with 284B total parameters and 13B activated per token, designed for fast inference and high-throughput workloads. It supports a 1M-token context window, enabling large-scale reasoning and long-context processing. Built with hybrid attention for efficiency, the model maintains strong performance in reasoning and coding while offering configurable reasoning modes. It is well suited for coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are critical. |