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 VL (Free)nemotron-3.5-super-vl:free | Whisper Large V3 Turbowhisper-large-v3-turbo |
|---|---|---|
| Pricing | ||
| Input | $0 / 1M | $3.33 / 1M |
| Output | $0 / 1M | — Not priced per output token |
| Cache Write | $0 / 1M | — |
| Cache Read | $0 / 1M | Not applicable |
| Cache Write (5m) | — | Not applicable |
| Cache Write (1h) | — | Not applicable |
| Web Search | — | $0 / 1M |
| Context | ||
| Max context | 1M | N/A |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | No |
| JSON Mode | Yes | No |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | NVIDIA | OpenAI |
| Category | chat | voice |
| Charge type | Free | 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. | Whisper Large V3 Turbo is an optimized version of OpenAI's Whisper Large V3 speech recognition model, designed for high-speed and cost-efficient transcription. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With a ~12% word error rate and real-time speed factors up to 216×, it delivers fast, scalable performance for latency-sensitive and high-throughput transcription workloads, making it ideal for real-time and large-scale speech processing applications. |