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 | Universal-3.5 Prouniversal-3-5-pro | GPT-6 Solgpt-6-sol | Gemini 3.5 Flashgemini-3.5-flash |
|---|---|---|---|
| Pricing | |||
| Input | — Not priced per input token | $2.00 / 1M | $1.50 / 1M |
| Output | — Not priced per output token | $10.00 / 1M | $9.00 / 1M |
| Cache Write (5m) | Not applicable | $2.00 / 1M | $1.50 / 1M |
| Cache Write (1h) | Not applicable | $2.00 / 1M | $1.50 / 1M |
| Cache Read | Not applicable | $2.00 / 1M | $1.50 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | 1.1M | 1M |
| 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 | AssemblyAI | OpenAI | |
| Category | voice | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-09-22 | — | — |
| Description | |||
| Summary | AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio. | GPT-6 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applications | Gemini 3.5 Flash is Google's high-efficiency multimodal model, delivering near-Pro level performance in coding and reasoning at Flash-tier speed and cost. It supports text, image, video, audio, and PDF inputs, making it well suited for diverse multimodal workflows. Optimized for coding proficiency and parallel agentic execution, the model defaults to medium thinking effort for faster, cost-efficient responses while supporting configurable thinking levels (minimal, low, medium, high) for fine-grained cost–performance control. |