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 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertex | Muse Wondermuse-wonder | Qwen3 Max Thinkingqwen3-max-thinking |
|---|---|---|---|
| Pricing | |||
| Request | $0.0336 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | $4.00 / 1M | $1.20 / 1M |
| Cache Write (1h) | Not applicable | $4.00 / 1M | $1.20 / 1M |
| Cache Read | Not applicable | $4.00 / 1M | $1.20 / 1M |
| Input | — | $4.00 / 1M | $1.20 / 1M |
| Output | — | $20.00 / 1M | $6.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 262.1K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | No | Yes | Yes |
| JSON Mode | No | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | Meta | Alibaba | |
| Category | image | chat | chat |
| Charge type | Pay Per Request | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size. | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. | Qwen3-Max-Thinking is the flagship reasoning model in the Qwen3 series, designed for high-stakes cognitive tasks that require deep, multi-step reasoning. By significantly scaling model capacity and reinforcement learning compute, it achieves substantial improvements in factual accuracy, complex reasoning, instruction following, alignment with human preferences, and agentic behavior. Optimized for advanced problem solving and long-horizon tasks, Qwen3-Max-Thinking is well suited for research, complex analysis, and agentic applications where reliability and structured reasoning are critical. |