DeepSeek V3: Specs, Pricing & Key Features

DeepSeek V3 is the open-weight model DeepSeek released on December 26, 2024, that went on to trigger one of the biggest market shocks in AI history the following month. Built as a 671-billion-parameter Mixture-of-Experts model trained for a fraction of the cost of comparable Western models, it proved open-weight labs could match closed frontier performance. This guide covers its specs, architecture, benchmarks, and where it stands in DeepSeek's lineup today.

DeepSeek V3 is a 671-billion-parameter Mixture-of-Experts (MoE) language model with about 37 billion parameters active per token, trained on 14.8 trillion tokens. It's a general-purpose model for chat, coding, and reasoning tasks, released under a permissive license that allows commercial use and self-hosting from open weights.

Its release in late December 2024 drew relatively little attention at first. That changed in January 2025, when DeepSeek's low training cost and strong benchmark results — combined with the free DeepSeek app topping app store charts — wiped hundreds of billions of dollars off US tech stocks in a single trading day, an event widely referred to as the "DeepSeek moment."

V3 uses DeepSeekMoE with an auxiliary-loss-free load-balancing strategy, meaning only a fraction of its 671B parameters activate for any given token. Combined with Multi-head Latent Attention (MLA) for compressed key-value caching, this kept training and inference costs far below what a dense model of similar capability would require.

V3 was trained with a Multi-Token Prediction objective, having the model predict several future tokens at once during training rather than just the next one — improving both training signal density and inference speed. DeepSeek also trained the model using FP8 mixed-precision, an efficiency choice that contributed to its comparatively low reported training cost.

At release, DeepSeek reported V3 matching or beating leading closed models of the time on coding, math, and general knowledge benchmarks, while costing a fraction as much to train and run. That combination — open weights, permissive licensing, and near-frontier benchmark scores — is what made it a reference point for the rest of the industry.

DeepSeek iterated quickly on V3 through 2025 — a March 2025 refresh, then V3.1 and V3.1-Terminus, then V3.2 with sparse attention for cheaper long context — before replacing the lineup entirely with DeepSeek V4 in April 2026. The original V3 checkpoint is no longer the recommended model for new projects, but its open weights remain downloadable for anyone who wants to self-host it.

The original V3 checkpoint is largely of historical and research interest today, but it still has a few practical niches:

• Self-hosted deployments. Its permissive license and open weights let teams run it entirely on their own infrastructure with no per-token API cost.
• Research and benchmarking. As a well-documented, widely studied checkpoint, V3 is a common baseline in academic papers comparing MoE architectures and training efficiency techniques.
• Budget-constrained general tasks. For teams already running V3 in production, it remains a capable general-purpose model for chat, writing, and basic coding.
• Learning MoE architecture. Its published technical report is a widely cited reference for engineers studying auxiliary-loss-free load balancing and multi-token prediction.

The original DeepSeek V3 isn't in Chat Smith's model lineup. The platform has moved on to DeepSeek's current generation instead: DeepSeek V4 Pro and DeepSeek V4 Flash, both with a 1M-token context window and stronger benchmark scores than V3 ever posted.

• Use DeepSeek V4 Pro for the same low-cost, open-weight philosophy V3 pioneered, with far more capability
• Compare its answers against Claude Sonnet 5 or GPT-5.6 Sol
• Switch models mid-conversation without losing your chat history

DeepSeek V3 mattered less for what it could do at launch and more for what it proved: that an open-weight model trained on a comparatively small budget could rival closed frontier labs, reshaping how the whole industry thought about training efficiency. It's since been superseded by V3.2 and then V4, which carry the same low-cost philosophy forward with far more capability. You can try DeepSeek's current models today on Chat Smith.