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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 | Qwen3.5-Flashqwen3.5-flash-02-23 |
|---|---|---|
| Pricing | ||
| Input | $0.10 / 1M | $0.10 / 1M |
| Output | $0.40 / 1M | $0.40 / 1M |
| Cache Write (5m) | $0.10 / 1M | $0.10 / 1M |
| Cache Write (1h) | $0.10 / 1M | $0.10 / 1M |
| Cache Read | $0.10 / 1M | $0.10 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | NVIDIA | Alibaba |
| Category | chat | chat |
| Charge type | Pay As You Go | 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. | Qwen3.5 Vision-Language Flash models are built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to achieve higher inference efficiency. Compared with the Qwen3 generation, the 3.5 Flash models deliver significant improvements in both pure-text reasoning and multimodal understanding. Optimized for fast response times, they strike a strong balance between inference speed and overall performance, making them well suited for real-time multimodal and agent-based applications. |