Chinese AI models gain ground across Africa

African technology developers are increasingly adopting Chinese artificial intelligence models as lower costs, open access and easier customisation reshape competition with more powerful proprietary systems developed in the United States.

Models from Alibaba, DeepSeek and Beijing-based Moonshot AI are becoming widely used foundations for applications in education, agriculture, healthcare and financial services. Their appeal is strongest among start-ups and research teams working with limited computing capacity, constrained funding and languages poorly represented in mainstream AI systems.

Alibaba’s Qwen, DeepSeek’s model family and Moonshot’s Kimi can be downloaded, modified and hosted on private infrastructure. This gives developers greater control over data and allows them to adapt the technology without repeatedly paying premium charges to overseas providers.

Ugandan researchers developing Sunflower LLM, a system designed to work with 31 local languages, built the project using Alibaba’s Qwen 3. The initiative illustrates how African developers are using Chinese foundation models to produce smaller, specialised systems rather than attempting to build expensive general-purpose models from the ground up.

Language coverage has emerged as a decisive factor. Africa is home to between 1,500 and 3,000 languages, but digital training material is scarce for many of them. Models trained primarily on English and other widely documented languages often perform poorly when processing local vocabulary, grammar and cultural context.

Adapting an AI system to an African language can cost between three and 30 times more than adapting it to English because words and expressions may require more tokens to process. Open-weight models reduce part of that burden by allowing engineers to alter the underlying system and train it on smaller collections of locally sourced material.

Chinese platforms are also competing aggressively on price. The latest DeepSeek V4-Flash model charges about $0.14 per million input tokens and $0.28 per million output tokens. Its average cost for completing a standard benchmark test has been estimated at roughly three US cents, substantially below comparable offerings from major US laboratories.

Alibaba has reinforced the trend with Qwen3.8-Max, a 2.4-trillion-parameter system that uses a mixture-of-experts design to activate only a fraction of its total parameters for each request. The approach reduces processing costs and latency while retaining the scale required for demanding text, image and video tasks.

Chinese models overtook US-developed systems in open-model adoption during 2025 and widened their lead during the first quarter of 2026. Qwen had accumulated more than 942 million downloads by March, while developers had created more than 200,000 model repositories connected to its architecture.

Africa’s expanding AI ecosystem provides a large potential market. A survey of the sector tracked 207 AI start-ups operating in 17 countries during 2025, almost double the 104 recorded three years earlier. Nigeria, South Africa and Kenya accounted for 63 per cent of the companies, with emerging clusters in Egypt, Ghana, Tunisia and Rwanda.

Finance, agriculture, healthcare and education collectively represented more than a third of the start-ups. These sectors favour models that can be fine-tuned for narrow tasks, including crop advice, medical triage, credit assessment, translation and examination preparation.

Chinese technology groups are supporting adoption through cloud infrastructure, training programmes and developer competitions. Huawei has promoted cloud packages incorporating DeepSeek, while Alibaba has marketed Qwen through its cloud network and ModelScope developer platform. Beijing also launched a programme offering young African developers training and study visits to China.

The strategy resembles the earlier expansion of Chinese telecommunications equipment and smartphones across the continent. Affordable devices tailored to local requirements allowed manufacturers such as Transsion to capture a large share of Africa’s handset market before several Western competitors recognised its commercial potential.

US companies retain clear advantages in frontier-model performance, investment capacity and global enterprise relationships. Google’s smaller Gemma models have also attracted African researchers because they can run on devices with limited computing power and support both speech and text applications. Rwanda has pursued cooperation with Anthropic for public administration and education, showing that Chinese suppliers do not hold an uncontested position.

Security and governance questions remain unresolved. Open models can be operated locally, limiting the need to transfer sensitive information to an overseas provider, but developers must still assess how training data, embedded political restrictions and software updates could affect their systems.