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 | Claude Fable 5.1claude-fable-5.1 | Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free |
|---|---|---|---|
| Pricing | |||
| Input | — Not priced per input token | $10.00 / 1M | $0 / 1M |
| Output | — Not priced per output token | $50.00 / 1M | $0 / 1M |
| Cache Write (5m) | Not applicable | $12.50 / 1M | — |
| Cache Write (1h) | Not applicable | $20.00 / 1M | — |
| Cache Read | Not applicable | $0.25 / 1M | $0 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 1M |
| 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 | Anthropic | NVIDIA |
| Category | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Free |
| 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. | Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications. | 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. |