DeepSeek V4 Pro

DeepSeek-V4.1-Flash-1017 is the October 17, 2026 update to DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model that natively accepts text and images, with a 1M-token context window and MIT license. It improves reasoning, coding, agentic tasks, and visual benchmarks over V4.1 Flash while reducing task execution time and output length, so tasks finish faster and cheaper. DeepInfra serves it in fp8 with JSON output, function calling, and zero data retention, plus Priority and Flex service tiers. DeepSeek reports DeepSWE v1.1 at 77.0 (up from 74.2) and Terminal-Bench 4.0 at 42.3 (up from 31.2) versus V4.1 Flash.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone, activating just 8B parameters per token during prefill and 16B during decoding. It natively processes images and text, supports a 1M-token context window, and introduces a Causal Encoder-Decoder architecture with Compressed Sparse Attention 2 to make long-context inference more efficient. Configurable reasoning-effort levels let developers trade latency and cost for deeper deliberation. Compared with DeepSeek-V4-Flash-0731, it reduces the global KV-cache footprint by roughly 4x while delivering stronger coding, reasoning, and agentic performance.

DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts large language model from DeepSeek with a 1M-token context window and strong general, coding, and reasoning performance. It is designed as a fast, low-cost frontier model suitable for long-context chat, agents, and tools. The Flash variant emphasizes high throughput and low latency while maintaining competitive reasoning quality.

A frontier-level reasoning model with 671B total parameters (37B activated per token) that delivers performance comparable to OpenAI's o1 across mathematics, coding, and reasoning tasks. Licensed under MIT for free commercial use, it pioneered cold-start data techniques before reinforcement learning to achieve breakthrough capabilities.

DeepSeek V3.1 is an advanced open-source hybrid AI model designed to balance powerful reasoning with high-speed efficiency. It uniquely supports two inference modes—"Think" for deep reasoning and "Non-Think" for direct, lightweight tasks—making it versatile across use cases. Built with a mixture-of-experts architecture, it scales to 685B parameters while activating only 37B per token, enabling cost-effective performance. With a 128K context window, it can handle large documents, codebases, and complex multi-step workflows. DeepSeek V3.1 is optimized for tool use, agent-based applications, and enterprise deployment through open weights and developer-friendly APIs.

DeepSeek-V3.2 is positioned as a next-generation “general-purpose + reasoning” model intended to be a daily driver at roughly frontier (GPT‑5-class) performance on broad tasks like coding, math, agents, and general chat. It is released in several variants (such as V3.2, V3.2-Exp, and V3.2-Speciale), sharing the same core architecture but targeting different trade‑offs between efficiency and maximum reasoning power.