As the 2026 FIFA World Cup brings together more nations, more matches and more excitement, a competition has intensified off the pitch among China's leading artificial intelligence models, which are being put to the test in predicting the results of one of the world's biggest sporting events.

随着2026年FIFA世界杯汇聚更多国家、更多比赛和更多激情，场外一场竞赛也在中国领先的人工智能模型之间愈演愈烈——它们正接受着预测全球最大体育赛事结果的考验。

The 23rd edition of the World Cup, featuring 48 teams, is being hosted by the United States, Canada and Mexico.
第23届世界杯由美国、加拿大和墨西哥联合主办，共有48支球队参赛。

It opened on Thursday and runs through July 19.
赛事于6月11日开幕，将持续至7月19日。

Several Chinese large language models, including Qwen, DeepSeek, Kimi and MiniMax, have rolled out prediction features, turning the tournament into a new testing ground for AI-powered reasoning and data analysis.
通义千问、DeepSeek、Kimi和MiniMax等多家中国大语言模型已推出预测功能，将本届赛事变成AI推理与数据分析的新试验场。

"As one of the most-watched sporting events across the globe, the World Cup offers AI companies a rare opportunity to showcase the computing power and analytical skills of their LLMs to a wider audience," said Guo Tao, a member of the Chinese Association for Artificial Intelligence and a senior expert in AI.
中国人工智能学会会员、资深AI专家郭涛表示：“作为全球关注度最高的体育赛事之一，世界杯为AI企业提供了一个难得的机会，向更广泛的受众展示其大语言模型的计算能力和分析技巧。”

Several AI platforms have come up with interactive campaigns.
多家AI平台推出了互动活动。

For instance, Moonshot AI's Kimi has launched a 1 trillion-token reward pool, allowing users to share prizes by correctly predicting match winners and the final champion.
例如，月之暗面的Kimi推出了1万亿token奖池，用户可通过正确预测比赛胜负和最终冠军来分享奖励。

A token refers to the smallest unit of data processed by AI models.
Token指的是AI模型处理的最小数据单元。

Alibaba Group's Qwen has introduced a dedicated match prediction assistant, while also offering human-versus-AI prediction challenges.
阿里巴巴集团的通义千问推出了专门的赛事预测助手，同时提供人机预测挑战。

However, the World Cup has also exposed the limitations of current AI models when it comes to analyzing and predicting the results of sporting events.
然而，世界杯也暴露了当前AI模型在分析和预测体育赛事结果方面的局限性。

For example, before the Group C opener between Brazil and Morocco on Sunday, major LLMs made predictions in favor of Brazil based on both historical data and statistical indicators.
例如，在6月14日巴西与摩洛哥的C组揭幕战前，各大模型根据历史数据和统计指标均预测巴西获胜。

The match ended in a 1-1 draw.
然而，比赛最终以1比1平局收场。

Guo said that while AI can analyze historical data and statistical models, it still struggles to accurately predict real-world results, especially in sports.
郭涛表示，虽然AI可以分析历史数据和统计模型，但在准确预测现实世界结果方面仍然力不从心，尤其是在体育领域。

He pointed out that soccer matches are influenced by a wide range of factors in the physical world, and such variables are highly uncertain and difficult to quantify using fixed AI models, making precise predictions inherently challenging.
他指出，足球比赛受现实世界中多种因素影响，这些变量高度不确定，难以用固定的AI模型量化，因此精确预测本身就极具挑战性。

The limitations of current AI models were also highlighted by Wang Zhongyuan, president of the Beijing Academy of Artificial Intelligence, at this year's BAAI Conference held last week.
北京智源人工智能研究院院长王仲远在上周举行的2026年智源大会上同样强调了当前AI模型的局限性。

Wang said that while LLMs have become increasingly capable of solving problems in the digital world, many challenges in the physical world remain beyond their reach.
王仲远表示，虽然大语言模型在解决数字世界的问题上能力日益增强，但物理世界中的许多挑战仍超出其能力范围。

As a result, the next stage of AI development will gradually shift from "predicting the next token" to "predicting the next physical state", he added.
因此，AI发展的下一阶段将逐渐从“预测下一个token”转向“预测下一个物理状态”，他补充道。

Asked why tech companies are rolling out AI prediction features for sports when the accuracy rate is relatively low, Guo, the expert from the Chinese Association for Artificial Intelligence, said the trend partly reflects the growing pressure of competition across the industry.
当被问及为何科技公司在准确率相对较低的情况下仍推出体育赛事AI预测功能时，中国人工智能学会专家郭涛表示，这一趋势在一定程度上反映了行业竞争压力日益增大。

"As competition in the LLM market intensifies, technological differentiation is becoming increasingly difficult. Companies are eagerly seeking new channels to distinguish themselves from their rivals," he said.
“随着大语言模型市场竞争加剧，技术差异化变得越来越困难。各家企业都在急切地寻找新的渠道来凸显自身与竞争对手的不同，”他说。

As the AI technology matures, simply competing on the size parameter is not enough, Guo said.
郭涛表示，随着AI技术日趋成熟，仅仅在参数规模上竞争已远远不够。

"The market is paying less attention to how large a model is and more attention to whether it can deliver valuable services in real-world scenarios and solve practical problems for users," he added.
“市场越来越不关注模型有多大，而是更关注它能否在现实场景中提供有价值的服务、解决用户的实际问题，”他补充道。

Hu Yanping, a professor at Shanghai University of Finance and Economics, said that LLMs and AI agents are already evolving from conversation-oriented systems into task-oriented systems, while moving beyond pretraining toward continuous learning and broader real-world perception.
上海财经大学教授胡燕平表示，大语言模型和AI智能体已开始从对话式系统向任务式系统演进，同时正从预训练阶段向持续学习和更广泛的现实感知能力迈进。

"Exploratory projects, such as World Cup match predictions, can help accelerate this evolution," Hu said.
“世界杯赛事预测等探索性项目可以加速这一演进进程，”胡燕平说。

"A capability framework built around perception, interaction, decision-making and collaboration is what future task-oriented AI agents need."
“围绕感知、交互、决策和协作构建的能力框架，正是未来任务式AI智能体所需要的。”