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 Previewgemini-3.1-flash-lite-preview | Muse Wondermuse-wonder |
|---|---|---|
| Pricing | ||
| Input | $0.25 / 1M | $4.00 / 1M |
| Output | $1.50 / 1M | $20.00 / 1M |
| Cache Write (5m) | $0.25 / 1M | $4.00 / 1M |
| Cache Write (1h) | $0.25 / 1M | $4.00 / 1M |
| Cache Read | $0.25 / 1M | $4.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1.0M | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Meta | |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Gemini 3.1 Flash Lite Preview is Google's high-efficiency model designed for high-volume and cost-sensitive workloads. It improves overall quality compared to Gemini 2.5 Flash Lite while approaching the performance of Gemini 2.5 Flash across key capabilities. The model delivers enhancements in audio input/ASR, RAG snippet ranking, translation, data extraction, and code completion, and supports configurable thinking levels (minimal, low, medium, high) for flexible cost–performance optimization. With pricing at roughly half the cost of Gemini 3 Flash, it is well suited for large-scale production deployments. | 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. |