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| Attribute | Qwen3.5 Plus 2026-02-15qwen3.5-plus-02-15 | Gemini 4 Argongemini-4-argon |
|---|---|---|
| Pricing | ||
| Input | $0.26 / 1M | $2.00 / 1M |
| Output | $1.56 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.26 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.26 / 1M | $2.00 / 1M |
| Cache Read | $0.26 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Alibaba | |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Qwen3.5 Vision-Language Plus models are part of the native multimodal Qwen3.5 series, built on a hybrid architecture that combines linear attention mechanisms with sparse Mixture-of-Experts (MoE) designs to improve inference efficiency at scale. Across a wide range of evaluations, the series demonstrates performance comparable to leading state-of-the-art models. Compared with the Qwen3 generation, the 3.5 Plus models deliver significant improvements in both pure-text reasoning and multimodal understanding, making them well suited for applications that require strong performance across language, vision, and agent-based tasks. | 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. |