AI Benchmarks: Climbing the Wrong Hills

In 1975, Charles Goodhart, the Chief Adviser to the Bank of England, observed that when governments tried to regulate a set of financial assets, those assets stopped being reliable indicators of economic trends. In what is now known as Goodhart’s Law, he stated that:

“Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”

Put more simply, when a measure becomes a target, it ceases to be a good measure.

For most of AI’s history, AI benchmarks avoided this fate because they measured capabilities that existing models fell meaningfully short of and captured increases driven by a variety of model improvements. As researchers built better methods, higher scores followed not because they deliberately targeted those metrics, but as a side effect of genuine improvement.

This is no longer the case. Recent gains on benchmark performance appear concentrated in areas that frontier labs have explicitly targeted for improvement. Today’s models improve largely through reinforcement learning (RL): a model attempts a task many times, is scored on each attempt, and is pushed toward whatever scored highest, a process called hill climbing. Flawed benchmarks, therefore, not only mismeasure progress against real-world outcomes, but can steer models in the wrong direction.

As AI encompasses a growing share of the global economy, Goodhart’s Law is playing out at a scale it never has before. Big Tech alone plans to spend more than $700 billion on CapEx in 2026, much of it to build and serve models that are ranked, sold, and even bet on according to benchmark scores that often fail to measure what they claim to. That raises the question: are today’s benchmarks still the best way to measure frontier capabilities, or is it time for a new generation? This report covers how and why popular benchmarks are failing, why good ones are so expensive to build, and what better ones could look like.

Few benchmarks show the problem as clearly as Arena (formerly LMArena), a benchmark known for how deliberately labs target it. Launched in May 2023 by UC Berkeley researchers as Chatbot Arena, Arena’s namesake benchmark consists of a platform where users can chat with two anonymous models and vote on which response they prefer in a head-to-head battle. These votes are then converted into a chess-style Elo rating, allowing for direct comparisons of model performance across matchups. By April 2025, the platform drew over one million unique visitors per month and became a mainstay in new-model press releases.

In April 2025, Meta released Llama 4. In the company’s blog post announcing the new model family, it touted that Llama 4 Maverick, a 17 billion active parameter model, beat GPT-4o and Gemini 2.0 Flash across a wide range of benchmarks. One such headline number was an experimental chat version of Llama 4 Maverick achieving an Elo of 1417 on Arena, placing it second on the leaderboard and ahead of models released just months prior, including GPT-4.5, Grok 3, and DeepSeek R1.

In the days after the model’s release, users found the benchmark scores didn’t reflect reality. A disconnect emerged between the behavior of the model developers could download and the overly verbose, emoji-riddled responses observed in the Maverick version used for head-to-head preference battles on Arena. Users on X quickly pointed out the Maverick version used for the benchmark was “optimized for conversationally,” an assumption found in a chart footnote. At the same time, rumors spread that Meta trained on benchmark test sets, claims the company denied.

In response to growing criticism, Arena retested the unmodified version of Maverick accessible to the general public. It ranked 32nd, below GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, the last of which was released over a year before Llama 4. At the end of April 2025, Meta’s CEO, Mark Zuckerberg, admitted that:

“It was relatively easy for our team to tune a version of Llama 4 Maverick that could be way at the top. But the version we released, the pure model, actually has no tuning for that at all, so it’s further down. So you just need to be careful with some of these benchmarks.”

The very same day, researchers released a paper disclosing findings that Meta had privately tested 27 unreleased variants on Arena before launch. The process worked like p-hacking, where researchers can cherry-pick variables, outcomes, or data points to force a statistically significant result (i.e., p-value < 0.05). Just as a scientist who runs enough analyses will eventually find a “significant” result by chance, a lab that tests enough model versions will eventually find one that performs well on the leaderboard.

Arena shared the skepticism directed at Meta. Three days after Maverick’s launch, Gwern, the pseudonymous researcher and writer, flagged major issues with Arena that made it gameable. The people actually rating model responses are all self-selected, and since they are unpaid, they have zero incentive to be honest, rigorous in their judgments, or provide good responses. From this analysis, he posited that:

“It's past time for LMArena people to sit down and have some thorough reflection on whether it is still worth running at all, and at what point they are doing more harm than good.”

The failed launch of Meta’s Maverick model catalyzed a company restructuring, marking the beginning of over a year of reorganization and changes. In May 2025, Meta reportedly delayed the release of its flagship Llama 4 Behemoth, rumored in part because it would not match earlier performance claims. In June 2025, Meta invested $14.3 billion in Scale AI to secure a 49% stake and bring its CEO, Alexandr Wang, into Meta’s leadership to lead the newly formed Meta Superintelligence Labs. That same month, Meta began offering multimillion-dollar pay packages to attract AI research talent. In October 2025, it laid off approximately 600 workers across the AI teams working on pre-Maverick research and products.

In November 2025, Meta’s Chief AI Scientist, Yann LeCun, left the company, later admitting that Llama 4’s “results were fudged a little bit,” with different models being used to achieve better results on different benchmarks. He also underscored the role of Maverick in kickstarting the company-wide changes, stating that:

“Mark was really upset and basically lost confidence in everyone who was involved in this. And so basically sidelined the entire GenAI organization. A lot of people have left, a lot of people who haven’t yet left will leave.”

18 months after Maverick’s launch, the arena that Meta has climbed back to the podium of is little changed from what made it susceptible in the first place. The votes used to calculate model Elo scores still come from anonymous, unpaid Internet users who write their own prompts. Although Arena now filters out duplicate votes and adjusts for style differences by default, it has not changed who gets to cast the votes. The same people who pushed Maverick’s sycophantic behavior to a silver medal are judging today’s model responses. Frontier labs are also still permitted to test an uncapped number of model variants before making them officially public. The results from these unreleased versions are shared privately with the lab, and the lab can withdraw any model version it does not like.

In April 2026, a full year after the release of the Llama 4 model family, Meta introduced Muse Spark. This was followed by the release of Muse Spark 1.1, 1.2, 1.3, and the Muse Wonder models in successive months starting in July 2026. As of October 2026, Meta is once again second on the Arena text leaderboard for all labs, trailing only Anthropic, and Arena has raised $200 million at a $3.1 billion valuation in a Series B funding round.

The same benchmark is even being used to settle Polymarket and Kalshi markets. From February 2025 to October 2026, over $225 million traded across 130+ Polymarket markets on what the “best” AI model would be, with the winner decided by the Arena leaderboard. These markets even resolve without controlling for style differences, meaning they use the same flawed scoring methodology that carried Maverick to second place.

People closest to the models have also grown skeptical, with some researchers proclaiming their low confidence in benchmarks. Yet the pull to climb these leaderboards hasn’t weakened. Researchers still face internal pressure from company executives to improve scores on certain benchmarks, and launch-day performance announcements are still treated as a marketing exercise.

Meta is not the only lab whose benchmark scores have outrun its model. In July 2025, Elon Musk sold Grok 4 as “the smartest AI in the world” on the strength of its Humanity’s Last Exam (HLE) score, despite the fact that it failed in real-world tasks, where it was rated 66th compared to other AI models by human users (in addition to facing criticism for problematic responses to questions about controversial topics).

A year later, claims of new state-of-the-art coding performance accompanied Anthropic’s release of Claude Opus 5, which later faced complaints about jargon-heavy, verbose responses users found disruptive to real software engineering work. It even prompted some to combat overstylized responses by requesting answers in Simplified Technical English (ASD-STE100), a standardized form of the language originally developed to make aircraft maintenance documentation more easily understandable to non-native speakers.

Most recently, at the end of September 2026, Google released Gemini 4 Argon in a landmark release that came nearly a year after the company’s last frontier model, Gemini 3. The announcement claimed the new model had achieved frontier capabilities in complex workflows across knowledge work and software engineering. Soon after the release, however, it faced accusations of benchmaxxing, optimizing models for high benchmark scores at the expense of real-world usefulness. These suspicions stemmed mainly from reports that Google employees found the model still struggled on certain coding tasks, despite reportedly state-of-the-art performance on some agentic coding benchmarks.

When a model card claims a state-of-the-art capability, it typically supports the claim by citing a benchmark result. In this way, benchmarks serve as a scoreboard for frontier AI labs, with assessments of model quality and, ultimately, the ROI of investment in model development riding on the scores.

The importance of benchmarks has grown alongside the scale of AI CapEx. In 2025, Microsoft, Google, Amazon, and Meta spent nearly $400 billion on capital expenditures. After Q2 earnings, the companies raised their 2026 guidance to a collective $720 to $745 billion. OpenAI raised $122 billion at an $852 billion valuation in March 2026, and six months later, it was rumored to be aiming to raise a new funding round at a $1.4 trillion valuation. In May 2026, Anthropic raised $65 billion at a $965 billion valuation, and as of October 2026, its IPO could see it valued at more than $2 trillion.

Much of this value rests on OpenAI’s and Anthropic’s ability to sell model access to business customers. Enterprise spend on generative AI increased from $11.5 billion in 2024 to $37 billion in 2025, representing a 3.2x year-over-year increase. Of that, $12.5 billion went to foundation model APIs of labs like Anthropic, OpenAI, and Google. Reported model quality is critical to securing these contracts; enterprise buyers use model benchmarks as quasi-Magic Quadrants to filter models before testing them themselves.

Source: Artificial Analysis, Epoch AI, Surge AI, Vals AI, Zapier

Helping models rank higher on the scoreboard, and ostensibly improve model capabilities, has become big business. Meta’s $14.3 billion deal for a stake in Scale AI, a provider of post-training data and evaluation infrastructure, was only the start. Soon after, Surge AI, an e