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 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertex | Gemini 3.6 Flashgemini-3.6-flash | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Request | $0.067 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | $1.50 / 1M | $2.00 / 1M |
| Cache Write (1h) | Not applicable | $1.50 / 1M | $2.00 / 1M |
| Cache Read | Not applicable | $1.50 / 1M | $2.00 / 1M |
| Input | — | $1.50 / 1M | $2.00 / 1M |
| Output | — | $7.50 / 1M | $10.00 / 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 | No | Yes | Yes |
| Catalogue | |||
| Provider | |||
| Category | image | chat | chat |
| Charge type | Pay Per Request | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048. | Gemini 3.6 Flash is Google's high-efficiency model for coding, agentic workflows, and web and application development. It is optimized to produce polished, production-ready outputs with fewer unnecessary revisions, less hedging, and more direct task execution. By reducing both token usage and the number of model calls required to complete complex tasks, Gemini 3.6 Flash is well suited for high-throughput development, scalable agent systems, and cost-sensitive production workflows. | 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. |