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.
- DeepSeek V4.1 Flash 1017
- Nemotron 3.5 Super VL (Free)
- Nemotron 3.5 Super VL
- Muse Spark Open
- Gemini 3.1 Flash Lite Image (Google Vertex AI)
- Gemini 3.1 Flash Lite Image (Google AI Studio)Enterprise & partner access
- Gemini 3.1 Flash Image (Google Vertex AI)Enterprise & partner access
- Gemini 3.1 Flash Image (Google AI Studio)Enterprise & partner access
| Attribute | Kimi K3kimi-k3 | Gemini 4 Argongemini-4-argon | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $3.00 / 1M | $2.00 / 1M | $4.00 / 1M |
| Output | $15.00 / 1M | $10.00 / 1M | $20.00 / 1M |
| Cache Write (5m) | $3.00 / 1M | $2.00 / 1M | $4.00 / 1M |
| Cache Write (1h) | $3.00 / 1M | $2.00 / 1M | $4.00 / 1M |
| Cache Read | $3.00 / 1M | $2.00 / 1M | $4.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | MoonShot AI | Meta | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Kimi K3 is Moonshot AI's 2.8T-parameter open-weight multimodal reasoning model, designed for complex coding, knowledge work, and long-horizon agentic workflows. It excels at repository-scale development, tool use, debugging, and iterative problem solving across text, images, logs, tests, and runtime feedback. Built with KDA and Attention Residuals for improved computational efficiency, Kimi K3 delivers strong performance on advanced engineering and multimodal reasoning tasks, making it well suited for autonomous coding agents and large-scale production workflows. | 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. | 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. |