DeepSeek
DeepSeek closes the cost gap on reasoning models. Wherever cost matters more than the brand of the model, DeepSeek shows up.
Strong signal and real results. Worth committing a pilot to.
LLM·Open-source
What It Is
DeepSeek is a Chinese AI lab shipping open-weight reasoning-capable models at significantly lower cost than Western frontier models. DeepSeek R1, released in early 2025, was the first open reasoning model to match OpenAI's o-series on key benchmarks. The R1-0528 update and subsequent releases have continued to compete near the frontier while remaining freely available under permissive licences.
Why It Matters
For AEO, DeepSeek matters in two contexts. In Asian markets (especially China, but also parts of Southeast Asia), DeepSeek-powered answer engines often dominate where Western models have limited or no access. If your business sells into those markets, DeepSeek visibility is a different optimisation surface than Western model AEO.
In cost-sensitive enterprise deployments globally, DeepSeek is increasingly the substrate for internal copilots and embedded chat. That quietly broadens the AEO surface area in markets where you'd expect ChatGPT or Claude to dominate.
Key Developments
- 2025: DeepSeek R1 released as the first open reasoning model matching o1 benchmarks at a fraction of the cost.
- 2025: Rapid adoption in Asian-market answer engines and cost-efficient enterprise deployments.
- 2024: DeepSeek-V2 established the company as a credible open-weight competitor.
What to Watch
Watch DeepSeek's continued benchmark parity vs Western frontier models. The cost gap is the moat. Watch the regulatory environment around Chinese AI models in Western markets, which affects enterprise adoption. Track which vertical answer engines in your space are DeepSeek-powered. Their citation behaviour can differ markedly from Western models because of different training data and tuning priorities.
Strengths
- Cost advantage: Reasoning capability at significantly lower per-token cost than Western frontier models.
- Open weights: Like Llama, available for embedded and on-prem deployment.
- Asian-market reach: Default substrate for many answer engines in markets where Western models have limited presence.
- Reasoning-native: Built for chain-of-thought from the start, which suits long-form analytical answers.
Considerations
- Geopolitical risk: Western enterprise adoption is constrained by regulatory and procurement concerns around Chinese-origin models.
- Training-data biases: Different content priorities in training shape which sources DeepSeek prefers when answering. Western brand visibility can be lower.
- Less consumer surface: No major direct-to-consumer answer engine in Western markets. Impact is mostly indirect via embedded uses.
- Slower English-language release cadence: Benchmark and capability launches sometimes lag Chinese-language equivalents.
DeepSeek· Llama· Mistral· Claude· GPT-5 Family· Gemini 3.1 Pro· Reasoning Models· Amazon Nova