DeepSeek V4 drops 1T MoE, 1M context

// 45d agoMODEL RELEASE

DeepSeek V4 drops 1T MoE, 1M context

DeepSeek V4 enters the flagship arena with a 1-trillion parameter MoE architecture and "Engram" conditional memory supporting 1-million token context windows. Positioned as a direct rival to Claude 4.5 and GPT-5, it delivers frontier-grade coding performance at roughly 1/10th the cost of proprietary counterparts.

// ANALYSIS

DeepSeek is effectively commoditizing frontier intelligence, forcing Western labs into a high-margin corner while capturing the developer ecosystem via extreme price efficiency and open-weights accessibility.

–The 1T MoE architecture activates only 32-37B parameters per token, scaling reasoning capability without sacrificing inference speed or efficiency
–Engram memory achieves 97% accuracy on 1M-token "needle-in-a-haystack" tests, solving the retrieval degradation common in standard transformers
–Claimed 80-85% SWE-bench score suggests it may be the most capable model for real-world software engineering currently available to the public
–Aggressive API pricing ($0.14-$0.30 per 1M tokens) makes massive-scale agentic workflows economically viable for startups and hobbyists alike
–Weights expected under Apache 2.0 continue to empower the local LLM community, potentially enabling frontier-level intelligence on consumer multi-GPU setups

// TAGS

deepseekdeepseek-v4llmai-codingreasoningopen-weightsopen-source

DISCOVERED

45d ago

2026-04-24

PUBLISHED

45d ago

2026-04-24

RELEVANCE

10/ 10

AUTHOR

guiopen

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