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 | Kimi K3.1 Prokimi-k3.1-pro | Nemotron 3 Nano Omni (Free)nemotron-3-nano-omni-30b-a3b-reasoning:free |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | $0 / 1M |
| Output | $10.00 / 1M | $0 / 1M |
| Cache Write (5m) | $2.00 / 1M | — |
| Cache Write (1h) | $2.00 / 1M | — |
| Cache Read | $2.00 / 1M | $0 / 1M |
| Web Search | $0 / 1M | — |
| Cache Write | — | $0 / 1M |
| Context | ||
| Max context | 1M | 256K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | MoonShot AI | NVIDIA |
| Category | chat | chat |
| Charge type | Pay As You Go | Free |
| Released | — | — |
| Description | ||
| Summary | Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution. | NVIDIA Nemotron 3 Nano Omni is an open 30B-A3B multimodal model designed as a perception and context sub-agent for enterprise agent systems. It supports text, image, video, and audio inputs with text output, enabling unified multimodal reasoning within a single inference loop. Built on a hybrid MoE Transformer–Mamba architecture with Conv3D video layers and Efficient Video Sampling (EVS), it delivers significantly improved efficiency for video reasoning—achieving ~2× higher throughput and 2.5× lower compute compared to separate pipelines. With up to 300K context length and extended thinking support, it is well suited for scalable, multimodal agent workflows. |