ArticleSimon WillisonNews
DeepSeek continues to disrupt the frontier AI market by coupling open weights with aggressive pricing. Lower FLOP requirements and reduced KV-cache demands at 1M tokens make frontier-calibre MoE architectures much more accessible for self-hosting and cost-constrained deployments.
The V4 series demonstrates that architectural optimisation in Mixture-of-Experts routing and memory management can substantially cut API costs. As Willison shows in his price comparison, DeepSeek undercut offerings like GPT-5.4 Nano and Claude Sonnet 4.6 significantly. Although DeepSeek admits trailing the frontier by several months, their open MIT licensing and efficiency gains challenge proprietary closed-model pricing models.
Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.
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Article:DeepSeek AI
DeepSeek has open-sourced the preview release of DeepSeek-V4. The line-up features DeepSeek-V4-Pro and DeepSeek-V4-Flash, standardising a 1M context window and introducing architectural innovations to cut compute and memory overhead.
Link:deepseek-ai
DeepSeek has published the model card and weights for DeepSeek-V4-Pro-DSpark on Hugging Face. The release equips the DeepSeek-V4-Pro MoE model with an attached speculative decoding module (DSpark) for accelerated inference.
Link:deepseek-ai
DeepSeek-AI has officially released DeepSeek-V4-Flash-0731 on Hugging Face. The 304B-parameter model supersedes the preview version, featuring integrated DSpark speculative decoding and significantly enhanced agentic performance.
Link:deepseek-ai
DeepSeek-V4-Flash-0731 is a sparse Mixture-of-Experts language model featuring 13B active parameters and a 1-million-token context window. The NVIDIA Build model card outlines technical specifications, architecture details, and coding benchmark performances.
Link:Tencent Hy Team
The Tencent Hy Team has released Hy4-preview, an open-source Mixture-of-Experts (MoE) model featuring 770B total parameters and 49B activated parameters per token. The flagship model supports a 1M token context window and is tuned for software engineering, office analytics, and scientific research.
X post:Mia
DeepSeek has officially published the weights for DeepSeek-V4-Flash-0731 under an MIT licence on Hugging Face. The release supersedes the preview version, features enhanced agentic capabilities, and incorporates an attached speculative decoding module.
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