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 | DeepSeek R1T Chimeradeepseek-r1t-chimera | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | $0.30 / 1M | $2.00 / 1M |
| Output | $10.00 / 1M | $1.20 / 1M | $10.00 / 1M |
| Cache Write (5m) | $2.00 / 1M | $0.30 / 1M | $2.00 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.30 / 1M | $2.00 / 1M |
| Cache Read | $2.00 / 1M | $0.30 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 163.8K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | MoonShot AI | TNG Tech | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | 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. | DeepSeek-R1T-Chimera combines DeepSeek-R1 with V3-0324 using a MoE architecture, blending R1’s reasoning strengths with V3's token efficiency. Optimized for general text generation and instruction following, it balances reasoning, efficiency, and usability. The model is MIT-licensed and available for both research and commercial use. | 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. |