FullStackBench zh
Chinese subset of FullStackBench for evaluating end-to-end software engineering and full-stack development capability.
Qwen3.5-122B-A10B from Alibaba Cloud / Qwen Team currently leads the FullStackBench zh leaderboard with a score of 0.587 across 3 evaluated AI models.
What FullStackBench zh measures
FullStackBench zh is a text benchmark that evaluates large language models on reasoning, agents, and code tasks. LLM Stats tracks 3 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.6.
Compare leaders on the best AI for reasoning, best AI for agents and best AI for code leaderboards.
Qwen3.5-122B-A10B leads with 58.7%, followed by Qwen3.5-27B at 57.4% and Qwen3.5-35B-A3B at 55.0%.
Progress Over Time
Interactive timeline showing model performance evolution on FullStackBench zh
FullStackBench zh Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 122B | 262K | $0.40 / $3.20 | ||
| 2 | Alibaba Cloud / Qwen Team | 27B | 262K | $0.30 / $2.40 | ||
| 3 | Alibaba Cloud / Qwen Team | 35B | 262K | $0.25 / $2.00 |
FAQ
Common questions about FullStackBench zh.
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