Graphwalks parents <128k
A graph reasoning benchmark that evaluates language models' ability to find parent nodes in graphs with context length under 128k tokens, requiring understanding of graph structure and edge relationships.
GPT-5.4 from OpenAI currently leads the Graphwalks parents <128k leaderboard with a score of 0.898 across 11 evaluated AI models.
Progress Over Time
Interactive timeline showing model performance evolution on Graphwalks parents <128k
Graphwalks parents <128k Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 | OpenAI | — | 1.0M | $2.50 / $15.00 | ||
| 2 | OpenAI | — | 400K | $1.75 / $14.00 | ||
| 3 | OpenAI | — | — | — | ||
| 4 | OpenAI | — | 128K | $75.00 / $150.00 | ||
| 5 | OpenAI | — | 400K | $0.75 / $4.50 | ||
| 6 | OpenAI | — | 1.0M | $0.40 / $1.60 | ||
| 7 | OpenAI | — | 200K | $1.10 / $4.40 | ||
| 8 | OpenAI | — | 1.0M | $2.00 / $8.00 | ||
| 9 | OpenAI | — | 400K | $0.20 / $1.25 | ||
| 10 | OpenAI | — | 128K | $2.50 / $10.00 | ||
| 11 | OpenAI | — | 1.0M | $0.10 / $0.40 |
FAQ
Common questions about Graphwalks parents <128k.
More evaluations to explore
Related benchmarks in the same category
A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.
A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.
All 30 problems from the 2025 American Invitational Mathematics Examination (AIME I and AIME II), testing olympiad-level mathematical reasoning with integer answers from 000-999. Used as an AI benchmark to evaluate large language models' ability to solve complex mathematical problems requiring multi-step logical deductions and structured symbolic reasoning.
Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains
A verified subset of 500 software engineering problems from real GitHub issues, validated by human annotators for evaluating language models' ability to resolve real-world coding issues by generating patches for Python codebases.
Humanity's Last Exam (HLE) is a multi-modal academic benchmark with 2,500 questions across mathematics, humanities, and natural sciences, designed to test LLM capabilities at the frontier of human knowledge with unambiguous, verifiable solutions