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 | Perceptron Mk1.5perceptron-mk1.5 | Gemini 3.5 Flashgemini-3.5-flash |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | $0.15 / 1M | $1.50 / 1M |
| Output | $10.00 / 1M | $1.50 / 1M | $9.00 / 1M |
| Cache Write (5m) | $2.00 / 1M | $0.15 / 1M | $1.50 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.15 / 1M | $1.50 / 1M |
| Cache Read | $2.00 / 1M | $0.15 / 1M | $1.50 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 36.9K | 1M |
| 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 | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | Perceptron | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| 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. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | 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. |