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 Terragpt-6.1-terra | Qwen3.5-Flashqwen3.5-flash-02-23 | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Input | $1.00 / 1M | $0.10 / 1M | $2.00 / 1M |
| Output | $5.00 / 1M | $0.40 / 1M | $10.00 / 1M |
| Cache Write (5m) | $1.00 / 1M | $0.10 / 1M | $2.00 / 1M |
| Cache Write (1h) | $1.00 / 1M | $0.10 / 1M | $2.00 / 1M |
| Cache Read | $1.00 / 1M | $0.10 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | Alibaba | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost. | Qwen3.5 Vision-Language Flash models are built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to achieve higher inference efficiency. Compared with the Qwen3 generation, the 3.5 Flash models deliver significant improvements in both pure-text reasoning and multimodal understanding. Optimized for fast response times, they strike a strong balance between inference speed and overall performance, making them well suited for real-time multimodal and agent-based applications. | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. |