PeopleofSuperintelligence

Researcher

Nishant Relan

Nishant Relan is an AI researcher whose published work spans multimodal models and their evaluation. Reka listed him among the team behind Flash and Edge in February 2024, and he coauthored the Core, Flash, and Edge report in April 2024. In May 2024, he coauthored Vibe-Eval, an open benchmark with 269 visual prompts for multimodal chat. The paper reported that automatic scoring with Reka Core roughly correlated with human judgment.

Now at
Reka AI
Works
2
Citations
4
h-index
1

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. February 2024

    Reka Flash and Edge

    In February 2024, Reka releases Flash, a 21B multimodal language model, and the compact 7B Edge. The launch lists Relan among the Reka Team.

  2. April 2024

    The Core, Flash, and Edge report

    In April 2024, he coauthors a technical report on Reka Core, Flash, and Edge. The report says the models process and reason with text, images, video, and audio.

  3. May 2024

    An open benchmark for multimodal chat

    In May 2024, he coauthors Vibe-Eval, an open benchmark with 269 visual-understanding prompts, 100 of them hard. The authors release its evaluation code and data through a public repository.

  4. May 2024

    Scoring with Reka Core

    In May 2024, the paper proposes Reka Core as an automatic evaluator for multimodal chat models. Its scores roughly correlate with human judgment.

  5. May 2024

    Research at Reka

    Reka lists him on the Reka Team in February 2024. His 2024 publications cover the Core, Flash, and Edge model series and the Vibe-Eval benchmark.

Most-cited works

  1. Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models2024 · 3 citationsWith Aitor Ormazabal, Che Zheng, Cyprien de Masson d’Autume, Dani Yogatama, Donovan Ong and Max Bain
  2. Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models2024 · 1 citationsWith Aitor Ormazabal, Che Zheng, Cyprien de Masson d’Autume, Dani Yogatama, Donovan Ong and Max Bain

From OpenAlex, as of 21 October 2026.

  1. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Reka AI. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Reka AI. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. Companies supply the most prior-work hires at 7 of the 9 frontier AI labsIncludes Reka AI. Our estimates of the organizations where 550 people worked directly before joining 9 frontier AI labs across 620 career transitions, comparing the main feeder organizations per lab.
  4. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Reka AI. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Reka Flash: Efficient and Capable Multimodal Language Models reka.ai. Checked 20 October 2026.
  2. Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models arxiv.org. Checked 20 October 2026.
  3. Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models arxiv.org. Checked 20 October 2026.
  4. Nishant Relan csauthors.net. Checked 20 October 2026.
  5. GitHub - reka-ai/reka-vibe-eval: Multimodal language model benchmark, featuring challenging examples github.com. Checked 20 October 2026.