Intelligence index
Outperforms 87% of compared models

DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp.
It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time.
Outperforms 87% of compared models
Elo scores and win rates across design and agent tasks.
1362Elo
Win rate 46.7%
1300Elo
Win rate 53%
1244Elo
Win rate 51.7%
1300Elo
Win rate 53.4%
1220Elo
Win rate 50.8%
1289Elo
Win rate 47.9%
1274Elo
Win rate 51.2%
1263Elo
Win rate 43.3%
1280Elo
Win rate 51.2%