DeepSeek V3
DeepSeek's December 2024 frontier release: a 671-billion-parameter Mixture-of-Experts model approaching GPT-4o quality, with weights open and a published training cost of $5.5M — one to two orders of magnitude below US frontier estimates. Quietly released on December 26, 2024, it would become the foundation for R1 four weeks later and the source of the January 2025 Nvidia stock shock.
How are Intelligence, Speed & Cost bucketed?
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
DeepSeek V3, not R1, is the model that actually changed the economics. R1 added the reasoning headlines, but V3 was where the cost shock originated. Every subsequent debate about US export controls, AI capex sustainability, and Chinese AI capability has V3 as a central data point.
Core Capabilities
Context Window
Availability
Pricing Model
Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
What it feels like
- 671B-param MoE (37B active per token) — the open-weights model that finally matched GPT-4 / Claude 3.5 Sonnet quality
- 82.6% HumanEval (coding) and 90.2% MATH-500 — outperformed GPT-4o, Claude 3.5 Sonnet, and Llama 3 on each
- Trained for $5.576M on 2.788M H800 GPU hours — 1/100th of comparable proprietary training budgets
- Auxiliary-loss-free load balancing was a real architectural innovation, not just scaling
- MoE inference needs 8+ H100s; not laptop-friendly even though the weights are open
- MMLU score of 88.5 was the highest open-source result at release
Best use cases
- Self-hosted frontier-quality LLM for orgs that can't use proprietary APIs
- Math, coding, and general knowledge benchmarks at open-source price
- Fine-tuning and distillation into smaller production models
- Cost-sensitive bulk inference via budget providers
Tools to try
Not ideal for
- Edge / single-GPU deployments — the MoE size is large despite low active params
- Multimodal use cases — text-only
- Latency-sensitive interactive chat (better with smaller open models like Qwen 2.5)
Model Evolution
deepseek-v is DeepSeek's language model family.