Qwen3 32B

Leaderboards

Where Qwen3 32B sits among the top 20 in each category.

By task

Scored from votes on each kind of prompt alone, on the same 1,000-point scale.

Test results

Every test Qwen3 32B has a published result on, out of 100%, with its rank among the models that took it.

• AIME 2025

  #89 / 123

  Competition maths problems from the 2025 American Invitational Mathematics Exam.

  72.9%

  Self-reported

• LiveCodeBench

  #29 / 76

  LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.

  65.7%

  Self-reported

• AIME 2024

  #23 / 54

  American Invitational Mathematics Examination 2024, consisting of 30 challenging mathematical reasoning problems from AIME I and AIME II competitions. Each problem requires an integer answer between 0-999 and tests advanced mathematical reasoning across algebra, geometry, combinatorics, and number theory. Used as a benchmark for evaluating mathematical reasoning capabilities in large language models at Olympiad-level difficulty.

  81.4%

  Self-reported

• LiveBench

  #15 / 38

  LiveBench is a challenging, contamination-limited LLM benchmark that addresses test set contamination by releasing new questions monthly based on recently-released datasets, arXiv papers, news articles, and IMDb movie synopses. It comprises tasks across math, coding, reasoning, language, instruction following, and data analysis with verifiable, objective ground-truth answers.

  74.9%

  Self-reported · In the text score

• Arena Hard

  #2 / 27

  Arena-Hard-Auto is an automatic evaluation benchmark for instruction-tuned LLMs consisting of 500 challenging real-world prompts curated by BenchBuilder. It includes open-ended software engineering problems, mathematical questions, and creative writing tasks. The benchmark uses LLM-as-a-Judge methodology with GPT-4.1 and Gemini-2.5 as automatic judges to approximate human preference. Arena-Hard achieves 98.6% correlation with human preference rankings and provides 3x higher separation of model performances compared to MT-Bench, making it highly effective for distinguishing between models of similar quality.

  93.8%

  Self-reported

• CodeForces

  #17 / 20

  A competitive programming benchmark using problems from the CodeForces platform. The benchmark evaluates code generation capabilities of LLMs on algorithmic problems with difficulty ratings ranging from 800 to 2400. Problems cover diverse algorithmic categories including dynamic programming, graph algorithms, data structures, and mathematical problems with standardized evaluation through direct platform submission.

  0.0%

  Self-reported

• BFCL

  #6 / 11

  The Berkeley Function Calling Leaderboard (BFCL) is the first comprehensive and executable function call evaluation dedicated to assessing Large Language Models' ability to invoke functions. It evaluates serial and parallel function calls across multiple programming languages (Python, Java, JavaScript, REST API) using a novel Abstract Syntax Tree (AST) evaluation method. The benchmark consists of over 2,000 question-function-answer pairs covering diverse application domains and complex use cases including multiple function calls, parallel function calls, and multi-turn interactions.

  70.3%

  Self-reported

• Aider

  #4 / 4

  Aider is a comprehensive code editing benchmark based on 133 practice exercises from Exercism's Python repository, designed to evaluate AI models' ability to translate natural language coding requests into executable code that passes unit tests. The benchmark measures end-to-end code editing capabilities, including GPT's ability to edit existing code and format code changes for automated saving to local files. The Aider Polyglot variant extends this evaluation across 225 challenging exercises spanning C++, Go, Java, JavaScript, Python, and Rust, making it a standard benchmark for assessing multilingual code editing performance in AI research.

  50.2%

  Self-reported

• MultiLF

  #1 / 2

  MultiLF benchmark

  73.0%

  Unverified