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 | GPT-6.1 Solgpt-6.1-sol | Nemotron 3 Super (Free)nemotron-3-super-120b-a12b:free |
|---|---|---|---|
| Pricing | |||
| Input | — Not priced per input token | $2.00 / 1M | $0 / 1M |
| Output | — Not priced per output token | $10.00 / 1M | $0 / 1M |
| Cache Write (5m) | Not applicable | $2.00 / 1M | — |
| Cache Write (1h) | Not applicable | $2.00 / 1M | — |
| Cache Read | Not applicable | $2.00 / 1M | $0 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 262.1K |
| 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 | OpenAI | 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. | GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical. | NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid Mixture-of-Experts model designed for complex multi-agent and long-horizon reasoning workflows. It activates only 12B parameters per token, enabling high compute efficiency while maintaining strong accuracy on advanced tasks. Built on a hybrid Mamba–Transformer MoE architecture with multi-token prediction (MTP), the model delivers significantly higher token generation throughput than leading open models. It supports a 1M-token context window for long-context reasoning, cross-document analysis, and multi-step task planning. Trained with multi-environment reinforcement learning across diverse benchmarks—including AIME 2025, TerminalBench, and SWE-Bench Verified—Nemotron 3 Super achieves strong performance across reasoning and coding tasks. Released fully open with weights, datasets, and training recipes, it supports flexible customization and secure deployment from local workstations to cloud environments. |