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 | GPT-6.1 Solgpt-6.1-sol | Transcribe 1 Protranscribe-1-pro | Gemini Embedding 2gemini-embedding-2-preview |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | — Not priced per input token | $0.60 / 1M |
| Output | $10.00 / 1M | — Not priced per output token | $2.40 / 1M |
| Cache Write (5m) | $2.00 / 1M | Not applicable | $0.60 / 1M |
| Cache Write (1h) | $2.00 / 1M | Not applicable | $0.60 / 1M |
| Cache Read | $2.00 / 1M | Not applicable | $0.60 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1M | N/A | 8.2K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | No |
| Streaming | Yes | No | No |
| Catalogue | |||
| Provider | OpenAI | Fish Audio | |
| Category | chat | voice | embedding |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-24 | — |
| Description | |||
| Summary | 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. | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications. |