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 | DeepSeek V4.1 Flashdeepseek-v4.1-flash | mai-ds-r1:freeNot in the catalogue | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $0.30 / 1M | — | $4.00 / 1M |
| Output | $1.20 / 1M | — | $20.00 / 1M |
| Cache Write (5m) | $0.30 / 1M | — | $4.00 / 1M |
| Cache Write (1h) | $0.30 / 1M | — | $4.00 / 1M |
| Cache Read | $0.30 / 1M | — | $4.00 / 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 | DeepSeek | — | Meta |
| Category | chat | — | chat |
| Charge type | Pay As You Go | — | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion. | — | 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. |