DeepSeek V3

Summary

DeepSeek V3 delivers coding and math performance that rivals GPT-4o and Sonnet on public benchmarks at a fraction of their cost—but the gap between lab numbers and production reality is wider than the benchmark tables suggest. The real issues surface once you start shipping: capacity constraints that throttle during demand spikes, built-in censorship that persists even on self-hosted versions, and tool-use features that don't work as advertised on some inference platforms.

DeepSeek-V3 stands as the best-performing open-source model and exhibits competitive performance against frontier closed-source models. The 671B-parameter MoE architecture with 37B activated per token uses Multi-head Latent Attention and DeepSeekMoE for efficient inference and cost-effective training. In private benchmarks, DeepSeek v3 0324 ranks as the best non-reasoning model, even beating Claude 3.5 Sonnet. The architecture benefits from the latest training innovations and is the more future-proof option. For code generation, DeepSeek V3 is 29x cheaper than GPT-4o on per-token costs. Production constraints emerge at scale: the model refuses to answer roughly 85% of questions about politically sensitive topics due to Chinese regulatory requirements, self-hosting does not remove this censorship, only privacy. During demand spikes, DeepSeek suspended new account signups and halted API credit top-ups due to server capacity strain. Occasional insertion of Chinese text in code outputs disrupts workflows even at low temperature settings.

Bottom line: *Pick V3 if you need cheap, competitive coding and math for a team building internal tools where censorship won't surface. Switch away when you hit infrastructure limits, need agentic tool-calling at scale, or your workflow touches geopolitically sensitive content.*