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 | DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017 | Qwen3.5 Plus 2026-02-15qwen3.5-plus-02-15 | Kimi K3.1 Prokimi-k3.1-pro |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | $0.26 / 1M | $2.00 / 1M |
| Output | $0.50 / 1M | $1.56 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | $0.26 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | $0.26 / 1M | $2.00 / 1M |
| Cache Read | $0.10 / 1M | $0.26 / 1M | $2.00 / 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 | DeepSeek | Alibaba | MoonShot AI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis. | Qwen3.5 Vision-Language Plus models are part of the native multimodal Qwen3.5 series, built on a hybrid architecture that combines linear attention mechanisms with sparse Mixture-of-Experts (MoE) designs to improve inference efficiency at scale. Across a wide range of evaluations, the series demonstrates performance comparable to leading state-of-the-art models. Compared with the Qwen3 generation, the 3.5 Plus models deliver significant improvements in both pure-text reasoning and multimodal understanding, making them well suited for applications that require strong performance across language, vision, and agent-based tasks. | 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. |