Top AI Accuracy on LiveCodeBench Pro

High-forecast respondents often emphasize coding’s clear commercial value as a driver of progress: “Because coding assistance has an obvious commercial payoff, I expect sustained and significant effort from companies to push performance forward.” Some low-forecast respondents, meanwhile, question whether the “hard”, Olympiad-level LiveCodeBench question set is a good proxy for commercial potential, with one writing that there might not be “a lot of biz-use cases for these kinds of problems.”

Many low-forecast respondents emphasize that LiveCodeBench Pro’s continuously updated problem set creates a moving target and makes progress on the benchmark difficult: “An important variable is how much more difficult the test may become over time.” Other respondents acknowledge this dynamic but think “the ability to make harder problems will be outrun by AI progress.”

Respondents who forecast rapid LLM performance improvements on LiveCodeBench Pro often point to models’ technical advances. One writes, “By 2030, multiple generations of models and advances in planning, verification, and code agents could enable substantial improvement.” Another predicts that by 2030, “reasoning models will develop stronger internal self-verification: mental code tracing, multi-approach consistency checking, and symbolic reasoning about invariants.” Others point to reinforcement learning, test-time compute, and scaling as drivers of past and future progress. Conversely, several low-forecast respondents note that, according to the paper that launched LiveCodeBench, frontier models still “exhibit stark limitations in complex algorithmic reasoning, nuanced problem-solving, and handling edge cases” and express skepticism that current LLM architectures are capable of overcoming these limitations. One forecaster noted that “these problems require exceptional creative insight and non-obvious algorithmic leaps that current reasoning approaches struggle with, so progress will likely be slower than on easier tiers.”

High-forecast respondents frequently cite rapid improvements on related coding and mathematical benchmarks: “Frontier AI models made a roughly 16x jump in improvement between 2023 and 2024 on SWE bench.”; “I'll obtain a base rate from investigating FrontierMath performance over time. In November 2024, SOTA models couldn't solve almost any Tier 3-4 problems… Now, in November 2025, the best model scores around 18%.” Low-forecast respondents argue these benchmarks measure fundamentally different capabilities (e.g., “[C]urrent LLMs demonstrate proficiency in implementation-oriented problems; they exhibit stark limitations in complex algorithmic reasoning.”) or lack the adaptive difficulty component (e.g., “Unlike other benchmarks where models can be trained to the test, a continuously updated test may be more difficult to create rapidly escalating scores, and therefore may not generalize.”)

Multiple high-forecast respondents view recent progress as indicating the beginning of rapid improvement. One notes that “in the history of AI benchmarks, breaking the ‘zero barrier’ is often a leading indicator of a coming S-curve in performance,” and another that “once a capability threshold is breached (0% -> 5%), accuracy often doubles or triples within 12-18 months.”

Several high- and low-forecast respondents mention data-related concerns. A high-forecast respondent argues that “there will be some kind of leaking as [the] biggest models are constantly crawling the web. At some point they will get to similar code to the one in the benchmark and they will learn from it.” Another that “despite the claims of continuous updating to avoid contamination, I don't believe this benchmark will ultimately be able to avoid [it].” Low-forecast respondents express concern about data exhaustion, which they expect will hinder performance on LiveCodeBench: “Many advanced algorithmic tasks are underrepresented in public training data, so scaling models does not always produce corresponding improvements in functional code performance.”