DeepSeek-V3
Frontier 671B MoE base model trained for under $6M compute cost using FP8 mixed precision.
Technical Specification Matrix
Architecture & Engineering Overview
Mixture-of-Experts with Multi-head Latent Attention (MLA) and Multi-Token Prediction (MTP)
DeepSeek-V3 uses Multi-head Latent Attention (MLA) to compress KV caches by 93% and DeepSeekMoE auxiliary-loss-free load balancing. It established unprecedented training cost efficiency while competing at the highest tier of open foundation models.
Empirical Academic Benchmarks
DeepSeek-V3 Benchmark Performance
DeepSeek-V3 Technical Report (Dec 2024). 671B MoE base model.
Challenging competition-level high school and undergraduate mathematics problems evaluating step-by-step analytical proof capability.
Google-proof PhD-level multiple-choice questions in biology, physics, and chemistry designed by domain experts.
Expanded reasoning benchmark with 12,000+ challenging multi-step questions across 14 academic and professional subjects.
Human-verified subset of real-world GitHub issues evaluating autonomous agent capability to debug and resolve real open-source bugs.
Standard benchmark testing zero-shot Python function generation against functional unit test suites.
Verified Core Competencies
Head-to-Head Comparisons
Related Frontier Architectures
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Google DeepMindGemini 2.0 Pro Experimental
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