PeopleofSuperintelligence

Researcher

Guanting Chen

Guanting Chen is an AI researcher whose work spans large language models, code generation and machine reasoning. In 2024, he coauthored DeepSeek LLM, DeepSeek-Coder, DeepSeek-V2 and DeepSeek-V3. His later work included DeepSeek-R1, DeepSeek-V3.2, DeepSeek-V4 and the DeepSeek-V4.1-Flash report on KV cache compression. As of October 2026, he is affiliated with DeepSeek-AI in Beijing, following an earlier affiliation with Northwest University's School of Information Science and Technology in Xi'an.

Timeline

  1. January 2024

    DeepSeek LLM and Coder

    In January 2024, he coauthors DeepSeek LLM, a language-model project with a 2-trillion-token training dataset. He also coauthors DeepSeek-Coder, an open code-model series trained from scratch on 2 trillion tokens.

  2. May 2024

    The DeepSeek-V2 report

    In May 2024, he coauthors the DeepSeek-V2 report, which presents a mixture-of-experts language model with 236 billion total parameters and 21 billion activated for each token.

  3. December 2024

    The DeepSeek-V3 report

    In December 2024, he coauthors the DeepSeek-V3 technical report. The model uses 671 billion total parameters, with 37 billion activated for each token.

  4. January 2025

    DeepSeek-R1 reasoning

    In January 2025, he coauthors the DeepSeek-R1 report, which develops LLM reasoning through pure reinforcement learning. It describes self-reflection, verification and dynamic strategy adaptation as emergent reasoning patterns.

  5. December 2025

    The DeepSeek-V3.2 report

    In December 2025, he coauthors the DeepSeek-V3.2 report, which combines high computational efficiency with reasoning and agent performance. Its high-compute Speciale variant achieves gold-medal performance in both the 2025 International Mathematical Olympiad and the International Olympiad in Informatics.

  6. April 2026–September 2026

    DeepSeek-V4 and V4.1-Flash

    In April 2026, he coauthors the DeepSeek-V4 report, which describes models that support a one-million-token context. In September 2026, he coauthors DeepSeek-V4.1-Flash, whose compression design reduces the global KV cache to 890 bytes per token.

  7. As of October 2026

    DeepSeek-AI affiliation

    As of October 2026, he is affiliated with DeepSeek-AI in Beijing, following an earlier affiliation with Northwest University's School of Information Science and Technology in Xi'an. His DeepSeek work includes the DeepSeek-Coder report in January 2024 and DeepSeek-V4.1-Flash in September 2026.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

Photo: We have no published photo of Guanting Chen yet. Send us a free-licensed photo.

  1. DeepSeek LLM: Scaling Open-Source Language Models with Longtermism arxiv.org. Checked 19 October 2026.
  2. DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence arxiv.org. Checked 19 October 2026.
  3. Guanting Chen csauthors.net. Checked 19 October 2026.
  4. DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model arxiv.org. Checked 19 October 2026.
  5. DeepSeek-V3 Technical Report arxiv.org. Checked 19 October 2026.
  6. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning arxiv.org. Checked 19 October 2026.
  7. DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning nature.com. Checked 19 October 2026.
  8. DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models arxiv.org. Checked 19 October 2026.
  9. DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arxiv.org. Checked 19 October 2026.
  10. DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arxiv.org. Checked 19 October 2026.