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The Ouroboros of Benchmarking: Reasoning Evaluation in an Era of Saturation

İbrahim Ethem Deveci, Duygu Ataman

TL;DR

This paper interrogates whether current reasoning benchmarks meaningfully measure true reasoning in the era of saturated datasets and high-performance models. By analyzing 52 benchmarks across three major model families (OpenAI, Anthropic, Google) and organizing them into seven reasoning types, it reveals a rapid saturation cycle where older benchmarks quickly lose discriminative power and newer tasks become the new standard. The authors argue that improvements in benchmark scores may reflect dataset exposure and benchmark design more than robust, generalizable reasoning, and they advocate for formalized, task-specific evaluation frameworks and layered metrics beyond simple accuracy. The findings highlight the need for standardized, domain-informed evaluation practices to reliably track genuine reasoning capabilities and guide future model development.

Abstract

The rapid rise of Large Language Models (LLMs) and Large Reasoning Models (LRMs) has been accompanied by an equally rapid increase of benchmarks used to assess them. However, due to both improved model competence resulting from scaling and novel training advances as well as likely many of these datasets being included in pre or post training data, results become saturated, driving a continuous need for new and more challenging replacements. In this paper, we discuss whether surpassing a benchmark truly demonstrates reasoning ability or are we simply tracking numbers divorced from the capabilities we claim to measure? We present an investigation focused on three model families, OpenAI, Anthropic, and Google, and how their reasoning capabilities across different benchmarks evolve over the years. We also analyze performance trends over the years across different reasoning tasks and discuss the current situation of benchmarking and remaining challenges. By offering a comprehensive overview of benchmarks and reasoning tasks, our work aims to serve as a first reference to ground future research in reasoning evaluation and model development.

The Ouroboros of Benchmarking: Reasoning Evaluation in an Era of Saturation

TL;DR

This paper interrogates whether current reasoning benchmarks meaningfully measure true reasoning in the era of saturated datasets and high-performance models. By analyzing 52 benchmarks across three major model families (OpenAI, Anthropic, Google) and organizing them into seven reasoning types, it reveals a rapid saturation cycle where older benchmarks quickly lose discriminative power and newer tasks become the new standard. The authors argue that improvements in benchmark scores may reflect dataset exposure and benchmark design more than robust, generalizable reasoning, and they advocate for formalized, task-specific evaluation frameworks and layered metrics beyond simple accuracy. The findings highlight the need for standardized, domain-informed evaluation practices to reliably track genuine reasoning capabilities and guide future model development.

Abstract

The rapid rise of Large Language Models (LLMs) and Large Reasoning Models (LRMs) has been accompanied by an equally rapid increase of benchmarks used to assess them. However, due to both improved model competence resulting from scaling and novel training advances as well as likely many of these datasets being included in pre or post training data, results become saturated, driving a continuous need for new and more challenging replacements. In this paper, we discuss whether surpassing a benchmark truly demonstrates reasoning ability or are we simply tracking numbers divorced from the capabilities we claim to measure? We present an investigation focused on three model families, OpenAI, Anthropic, and Google, and how their reasoning capabilities across different benchmarks evolve over the years. We also analyze performance trends over the years across different reasoning tasks and discuss the current situation of benchmarking and remaining challenges. By offering a comprehensive overview of benchmarks and reasoning tasks, our work aims to serve as a first reference to ground future research in reasoning evaluation and model development.

Paper Structure

This paper contains 9 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Benchmark Landscape and Categorization
  3. Performance Trends Across Models
  4. Performance of Models within Benchmarks
  5. Discussion: Limitations of Current Benchmarking
  6. Limitations
  7. Conclusion
  8. Reasoning Benchmarks
  9. Performance of Models

Figures (6)

  • Figure 1: Number of benchmarks in different reasoning types over time.
  • Figure 2: Benchmark saturation dynamics.
  • Figure 3: Performance of the Claude family on reasoning benchmarks by category. Model numbers and corresponding names are as follows: 1 -- Claude 3 Haiku; 2 -- Claude 3 Sonnet; 3 -- Claude 3 Opus; 4 -- Claude 3.5 Haiku; 5 -- Claude 3.5 Sonnet; 6 -- Claude 3.7 Sonnet; 7 -- Claude 3.7 Sonnet (64K Extended Thinking); 8 -- Claude Sonnet 4; 9 -- Claude Opus 4; 10 -- Claude Opus 4.1.
  • Figure 4: Performance of the Gemini family on reasoning benchmarks by category. Model numbers and corresponding names are as follows: 1 -- Gemini Ultra; 2 -- Gemini Pro; 3 -- Gemini 1.5 Flash; 4 -- Gemini 1.5 Pro; 5 -- Gemini 2.0 Flash-Lite; 6 -- Gemini 2.0 Flash; 7 -- Gemini 2.5 Flash; 8 -- Gemini 2.5 Pro; 9 -- Gemini 2.5 Flash Lite (no thinking); 10 -- Gemini 2.5 Flash Lite (thinking).
  • Figure 5: Performance of the GPT family on general reasoning benchmarks. Model numbers and corresponding names are as follows: 1 -- GPT-3.5; 2 -- GPT-4; 3 -- GPT-4 Turbo; 4 -- GPT-4o mini; 5 -- GPT-4o; 6 -- o1-preview; 7 -- o1-mini; 8 -- o1; 9 -- o1-pro; 10 -- GPT-4.1 nano; 11 -- GPT-4.1 mini; 12 -- GPT-4.1; 13 -- GPT-4.5; 14 -- o3-mini; 15 -- o4-mini; 16 -- o3; 17 -- o3-pro; 18 -- gpt-oss-120b; 19 -- GPT-5 with Deep Research; 20 -- ChatGPT Agent; 21 -- GPT-5; 22 -- GPT-5 Pro.
  • ...and 1 more figures