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 | MiniMax M2minimax-m2 |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | — Not priced per input token | $0.15 / 1M |
| Output | $10.00 / 1M | — Not priced per output token | $0.45 / 1M |
| Cache Write (5m) | $2.50 / 1M | Not applicable | $0.15 / 1M |
| Cache Write (1h) | $4.00 / 1M | Not applicable | $0.15 / 1M |
| Cache Read | $0.20 / 1M | Not applicable | $0.15 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | N/A | 196.6K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | Anthropic | Fish Audio | MiniMax |
| Category | chat | voice | chat |
| 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. | MiniMax-M2 is a compact, high-efficiency model with 10B active (230B total) parameters, optimized for coding and agentic workflows. It delivers near-frontier reasoning and tool use, excels at multi-file coding tasks and compile-run-fix loops, and performs strongly on benchmarks like SWE-Bench and Terminal-Bench. It also handles long-horizon planning and recovery in agent evaluations, ranking among the top open models across reasoning domains. With fast inference and low cost, it’s ideal for large-scale agents and developer assistants — and works best when reasoning is preserved across turns. |