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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 Image (Google AI Studio)gemini-3.1-flash-image-aistudio |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | — |
| Output | $10.00 / 1M | — |
| Cache Write (5m) | $2.00 / 1M | Not applicable |
| Cache Write (1h) | $2.00 / 1M | Not applicable |
| Cache Read | $2.00 / 1M | Not applicable |
| Web Search | $0 / 1M | — |
| Request | — | $0.067 / request |
| Billing | — | Pay Per Request |
| Context | ||
| Max context | 1M | N/A |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | No |
| JSON Mode | Yes | No |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | MoonShot AI | |
| Category | chat | image |
| Charge type | Pay As You Go | Pay Per Request |
| 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 Image served from Google AI Studio. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048. |