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 | GPT Image 2 (OpenAI direct)gpt-image-2-openai | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free |
|---|---|---|
| Pricing | ||
| Input | $2.50 / 1M | $0 / 1M |
| Output | $15.00 / 1M | $0 / 1M |
| Cache Write (5m) | Not applicable | — |
| Cache Write (1h) | Not applicable | — |
| Cache Read | Not applicable | $0 / 1M |
| Cache Write | — | $0 / 1M |
| Context | ||
| Max context | 272K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | No | Yes |
| JSON Mode | No | Yes |
| Streaming | No | Yes |
| Catalogue | ||
| Provider | OpenAI | NVIDIA |
| Category | image | chat |
| Charge type | Pay As You Go | Free |
| Released | — | — |
| Description | ||
| Summary | Served by OpenAI directly. 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. Billed by tokens at OpenAI's price: text input US$5, image input US$8 and image output US$30 per 1M tokens. | 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. |