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 | Nemotron 3.5 Super VLnemotron-3.5-super-vl | GPT Image 2gpt-image-2 | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | — | $2.00 / 1M |
| Output | $0.40 / 1M | — | $10.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Cache Read | $0.10 / 1M | Not applicable | $2.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Request | — | $0.04 / request | — |
| Billing | — | Pay Per Request | — |
| Context | |||
| Max context | 1M | 272K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | NVIDIA | OpenAI | |
| Category | chat | image | chat |
| Charge type | Pay As You Go | Pay Per Request | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | 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. | GPT Image 2 combines OpenAI's GPT-5.4 with advanced image generation capabilities from GPT Image 2, enabling fully integrated multimodal workflows. It allows users to seamlessly transition between reasoning, coding, and visual generation within a single interaction, making it well suited for creative, development, and agent-driven applications that require both intelligence and visual output. | 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. |