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 | Perceptron Mk1.5perceptron-mk1.5 | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free |
|---|---|---|---|
| Pricing | |||
| Request | $0.067 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | $0.15 / 1M | — |
| Cache Write (1h) | Not applicable | $0.15 / 1M | — |
| Cache Read | Not applicable | $0.15 / 1M | $0 / 1M |
| Input | — | $0.15 / 1M | $0 / 1M |
| Output | — | $1.50 / 1M | $0 / 1M |
| Web Search | — | $0 / 1M | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | N/A | 36.9K | 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 | Yes | Yes |
| Catalogue | |||
| Provider | Perceptron | NVIDIA | |
| Category | image | chat | chat |
| Charge type | Pay Per Request | Pay As You Go | Free |
| Released | — | 2026-09-25 | — |
| 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. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications. |