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 | Claude Sonnet 5.5claude-sonnet-5.5 | 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) | $2.50 / 1M | Not applicable | $0.60 / 1M |
| Cache Write (1h) | $4.00 / 1M | Not applicable | $0.60 / 1M |
| Cache Read | $0.20 / 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 | Anthropic | 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 | Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor. | 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. |