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 AI Studio)gemini-3.1-flash-lite-image-aistudio | Muse Wondermuse-wonder | Magistral Medium 2506 (Thinking)magistral-medium-2506:thinking |
|---|---|---|---|
| Pricing | |||
| Request | $0.0336 / request | — | — |
| Billing | Pay Per Request | — | — |
| Cache Write (5m) | Not applicable | $4.00 / 1M | $2.00 / 1M |
| Cache Write (1h) | Not applicable | $4.00 / 1M | $2.00 / 1M |
| Cache Read | Not applicable | $4.00 / 1M | $2.00 / 1M |
| Input | — | $4.00 / 1M | $2.00 / 1M |
| Output | — | $20.00 / 1M | $5.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 41.0K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | No | Yes | No |
| JSON Mode | No | Yes | No |
| Streaming | No | Yes | No |
| Catalogue | |||
| Provider | Meta | Mistral AI | |
| 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 AI Studio. 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. | — |