AI Startup Trumps Google in Stanford's Model Rankings

Google’s PaLM 2 was beaten by Palmyra X V3, an open source model from AI startup Writer

Ben Wodecki, Deborah Yao

January 12, 2024

2 Min Read
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At a Glance

  • In a surprise, Writer's Palmyra X V3 outpaced Google's PaLM 2 in Stanford's leaderboard of foundation models.
  • A Chinese model from visionary Kai-Fu Lee also did well, while OpenAI's GPT-4 and GPT-4 Turbo predictably topped the chart.

In a surprise, a foundation model from AI startup Writer has trumped Google in the latest ranking of foundation model performance by Stanford University researchers.

Palmyra X V3 from AI startup Writer outperformed Google’s PaLM 2 as the highest-scoring non-OpenAI model on the leaderboard of Stanford’s Holistic Evaluation of Language Models (HELM) Lite.

Its 72 billion-parameter model outperformed rivals despite being much smaller than other models on the list. Palmyra ranked third on the HELM leaderboard, while PaLM 2 was fourth.

Another surprise was Yi-34B, an open source 34 billion-parameter model from, a Chinese startup founded by visionary Kai-Fu Lee. Trained on three trillion tokens, the model finished higher on Stanford’s leaderboard than Mistral 7B, Anthropic’s Claude 2 and even Meta’s Llama 2.

Predictably, OpenAI’s GPT-4 topped the Stanford list by a decent margin. The model, released last March, achieved top scores on benchmarks including OpenbookQA, for answering questions on elementary science facts, MMLU covering general standardized exam questions, and LegalBench, where the model performs tasks that require legal interpretation.

OpenAI’s GPT-4 Turbo came in second. Unveiled at DevDay 2023, the model was designed to be cheaper to run and capable of handling 16 times more text than GPT-4. GPT-4 Turbo finished lower than its earlier counterpart as it failed to follow instructions well.

Related:Leaderboard: OpenAI’s GPT-4 Has Lowest Hallucination Rate

Percy Liang, an associate professor of computer science at Stanford University, tweeted that smaller models “unexpectedly” outperformed the larger models. "Some recent models are very chatty: they sometimes output the correct answer in the wrong format, even when instructed to follow the format."

The HELM Lite test was designed to be lightweight yet broad. Stanford had previously published the HELM framework, with this latest test focusing solely on capabilities. The university’s researchers are planning to cover model safety in a new benchmark that is being developed in partnership with MLCommons.

HELM Lite tests models across a variety of capabilities such as machine translation, medicine and questions about books. The project was inspired by the Open LLM leaderboard from Hugging Face, in which Yi-34B currently sits at the top.

The Stanford team did not get access to closed-system models such as GPT-4 and Claude. Instead, they used the standard interfaces and carefully crafted their prompts to “coax them to generate outputs in the proper format.”

Read more about:

ChatGPT / Generative AI

About the Author(s)

Ben Wodecki

Jr. Editor

Ben Wodecki is the Jr. Editor of AI Business, covering a wide range of AI content. Ben joined the team in March 2021 as assistant editor and was promoted to Jr. Editor. He has written for The New Statesman, Intellectual Property Magazine, and The Telegraph India, among others. He holds an MSc in Digital Journalism from Middlesex University.

Deborah Yao


Deborah Yao runs the day-to-day operations of AI Business. She is a Stanford grad who has worked at Amazon, Wharton School and Associated Press.

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