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 | Gemini 3.5 Transcribegemini-3.5-transcribe | GPT-6.1 Solgpt-6.1-sol | GLM 5V Turboglm-5v-turbo |
|---|---|---|---|
| Pricing | |||
| Input | — Not priced per input token | $2.00 / 1M | $1.20 / 1M |
| Output | — Not priced per output token | $10.00 / 1M | $4.00 / 1M |
| Cache Write (5m) | Not applicable | $2.00 / 1M | $1.20 / 1M |
| Cache Write (1h) | Not applicable | $2.00 / 1M | $1.20 / 1M |
| Cache Read | Not applicable | $2.00 / 1M | $1.20 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 98.3K | 1M | 202.8K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | No | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | Z.AI | |
| Category | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-09-25 | — | — |
| Description | |||
| Summary | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | 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. | GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems. |