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 | Transcribe 1 Protranscribe-1-pro | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Request | $0.067 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | Not applicable | $2.00 / 1M |
| Cache Write (1h) | Not applicable | Not applicable | $2.00 / 1M |
| Cache Read | Not applicable | Not applicable | $2.00 / 1M |
| Input | — | — Not priced per input token | $2.00 / 1M |
| Output | — | — Not priced per output token | $10.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | N/A | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | No | No | Yes |
| JSON Mode | No | No | Yes |
| Streaming | No | No | Yes |
| Catalogue | |||
| Provider | Fish Audio | ||
| Category | image | voice | chat |
| Charge type | Pay Per Request | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-24 | — |
| 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. | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | 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. |