The New Open-source Landscape: Llama, DeepSeek and Others

Open-source large models are one of the most noteworthy variables in the 2026 AI industry.

The Ripple Effect of DeepSeek
Through MLA architecture and MoE sparsification, DeepSeek achieved near-closed-source-flagship reasoning at far below industry-expected costs. Its open-sourcing directly lowered the entire industry's inference costs and put "Chinese open source" center stage globally for the first time.

Llama Series: The Ecosystem Standard
Meta's Llama ecosystem is the largest, with the most comprehensive fine-tuning tools, quantization solutions, and edge deployment resources. For enterprises that want "ready to use," Llama remains the safest choice.

Qwen and GLM: Chinese-language Powerhouses
Qwen (Tongyi Qianwen) and Zhipu GLM excel at Chinese understanding and tool calling, and continue to open-source. They are the top candidates for Chinese enterprise private deployment.

New Players & Differentiation:
1. Mistral: European open-source representative, strong multilingual capability
2. Phi series: Microsoft's small-model approach, edge-deployment friendly
3. Various vertical open-source models: coding (DeepSeek-Coder), math, medical, etc.

Three Ways Open Source Changes the Industry:
1. Steep cost curve drop: Open source makes "building your own model" from impossible to evaluable
2. Data sovereignty returns: Enterprises can deploy privately, data never leaves the domain
3. Ecosystem competition replaces single-point competition: Whoever provides better toolchains, fine-tuning services, and compliance support wins

Advice for enterprises: First assess whether your business scenarios can be met by open-source models (especially inference tasks), then decide whether to purchase closed-source APIs.