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 2.5 Sunburstgpt-image-2.5-sunburst | Nemotron 3.5 Super VLnemotron-3.5-super-vl | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $5.00 / 1M | $0.10 / 1M | $4.00 / 1M |
| Output | $30.00 / 1M | $0.40 / 1M | $20.00 / 1M |
| Cache Write (5m) | Not applicable | $0.10 / 1M | $4.00 / 1M |
| Cache Write (1h) | Not applicable | $0.10 / 1M | $4.00 / 1M |
| Cache Read | Not applicable | $0.10 / 1M | $4.00 / 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 | OpenAI | NVIDIA | Meta |
| Category | image | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | OpenAI GPT Image 2.5 Sunburst. 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. | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. |