DeepSeek

Models & products

Models

DeepSeek

Trends

• Chinese labs move frontier training onto domestic chips
• AI labs move inference onto custom silicon
• Efficiency methods cut the compute and memory models need
• Chinese labs take the lead in open-weight models
• Price competition adds speed and time-of-day tiers

Items

1. Sep 17

   DeepSeek's V4.1-Flash technical report

   The report details a Causal Encoder-Decoder that activates 16B parameters at decode but only 8B at prefill, and a CSA2-plus-FP4 KV-cache design that cuts the in-HBM footprint to 890 bytes per token, roughly a quarter of V4-Flash's.

2. Sep 10

   DeepSeek-V4.1-Flash

   A 552B-parameter MoE on a new Causal Encoder-Decoder architecture with a 1M-token context and native multimodal input, released with MIT weights; DeepSeek says it surpasses V4-Pro and will reroute the flagship's traffic into it on September 14.

3. Aug 31

   DeepSeek-V4-Flash-Vision-Exp

   DeepSeek's first multimodal V4 model (~305B parameters, 13B active, 1M-token context, MIT license) adds a vision encoder tuned for agent perception of screenshots, interfaces, and charts, published with a full reference implementation ten days after its API debut.

4. Aug 12

   DeepSeek V4-Pro reaches general availability

   After a four-month preview, the flagship shipped with agent-focused re-post-training (Terminal-Bench 2.1 87.9, DeepSWE 62.7) at about $0.435/$0.87 per 1M tokens, roughly 46x cheaper than Claude Fable 5, with peak and off-peak pricing to follow.

5. Jul 10

   DeepSeek's DSpark speculative decoding

   A semi-autoregressive drafter with confidence-scheduled, load-aware verification speeds per-user generation 60-85% at matched throughput in DeepSeek-V4's live serving system.

6. Jul 8

   DeepSeek and Zhipu design in-house inference chips

   Reuters and The Information report both Chinese labs are quietly designing custom inference silicon to cut dependence on Nvidia and Huawei, with DeepSeek's move coinciding with its first outside fundraise of ~$7B.

7. May 22

   DeepSeek V4 reportedly moves to Huawei Ascend

   Chinese media report DeepSeek gave Huawei's Ascend 950PR early access to V4 ahead of NVIDIA, aiming to migrate training and inference from CUDA to CANN, with Alibaba, ByteDance, and Tencent reportedly ordering hundreds of thousands of chips contingent on the result.

8. Apr 24

   DeepSeek-V4-Pro and V4-Flash

   MIT-licensed open-weight MoE previews with a 1M-token context and switchable thinking modes: V4-Pro has 1.6T total and 49B active parameters at $1.74/$3.48 per 1M tokens, and V4-Flash has 284B total and 13B active at $0.14/$0.28.