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 | Transcribe 1 Protranscribe-1-pro | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free | Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b |
|---|---|---|---|
| Pricing | |||
| Input | — Not priced per input token | $0 / 1M | $1.80 / 1M |
| Output | — Not priced per output token | $0 / 1M | $5.40 / 1M |
| Cache Write (5m) | Not applicable | — | $1.80 / 1M |
| Cache Write (1h) | Not applicable | — | $1.80 / 1M |
| Cache Read | Not applicable | $0 / 1M | $1.80 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Cache Write | — | $0 / 1M | — |
| Context | |||
| Max context | N/A | 1M | 262K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | No | Yes | Yes |
| JSON Mode | No | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | Fish Audio | NVIDIA | Alibaba |
| Category | voice | chat | chat |
| Charge type | Pay As You Go | Free | Pay As You Go |
| Released | 2026-09-24 | — | — |
| Description | |||
| Summary | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | 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. | Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights. |