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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 | Gemini 3.1 Flash Lite Previewgemini-3.1-flash-lite-preview |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | $0.25 / 1M |
| Output | $10.00 / 1M | $1.50 / 1M |
| Cache Write (5m) | $2.00 / 1M | $0.25 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.25 / 1M |
| Cache Read | $2.00 / 1M | $0.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 1.0M |
| 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 | |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| 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. | 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. |