When Hype Replaces Objective Metrics — Who’s Bare Without a Ruler?
Haven’t we all encountered this scenario? A new AI model launches with bombastic marketing claiming it “crushes GPT-4”, yet two months later, even basic math problems stump it. Vendors flood the internet with flashy press releases, but you have no clear way to verify each model’s real strengths and weaknesses. Picking a large language model becomes a blind box game — you only discover the performance mismatch after investing time into it.
This frustration hits especially hard for enterprise teams rolling out production AI, conducting model selection, and calculating ROI. Without an objective, trusted measuring stick, every strategic decision devolves into guesswork.
This critical gap is exactly what SuperCLUE was built to fill.
A Chinese AI Benchmark Born in 2019
SuperCLUE traces its origins back to CLUE (Chinese Language Understanding Evaluation), launched in 2019, long before the large model boom swept the industry北京日报. Back then, the CLUE community pioneered scientific, standardized scoring for Chinese language models, rolling out foundational test suites including FewCLUE and ZeroCLUE to advance NLP evaluation standards year after year.
The 2023 generative AI explosion spurred CLUE’s full upgrade into SuperCLUE — a comprehensive evaluation benchmark exclusively built for general-purpose Chinese large models. Its core mission answers three pivotal industry questions:
- How capable are these domestic large models in real-world scenarios?
- Where do they stand relative to leading global international models?
- How wide is the performance gap between state-of-the-art LLMs and human reasoning?
It is far more than a simple ranked leaderboard; evaluation relies on a three-tiered, multi-dimensional testing framework.
A Three-Tiered Evaluation System Covering Every Key Capability
SuperCLUE’s assessment framework is split into three complementary benchmark modules, each measuring distinct dimensions of model performance:
1. SuperCLUE-Open (Open-Domain Multi-Turn Dialogue)
Simulates real-world free chat scenarios to grade multi-turn conversation coherence, contextual retention, and natural open-ended generation. In 2023’s early benchmark data, GPT-4 scored 94.64 in this segment, while most domestic models lagged far behind, scoring only 20–50 points.
2. SuperCLUE-Opt (Closed-Set Objective Proficiency Tests)
A closed-book standardized exam with over 3,700 multiple-choice questions, split into three core buckets:
- Basic core abilities (10 sub-tasks): Semantic comprehension, casual dialogue, long context retention, role-playing, general knowledge, creative writing, logical reasoning, code generation, mathematical computation, content safety guardrails
- Chinese native linguistic capabilities (10 sub-tasks): Character glyphs, Pinyin, classical poetry, couplets, idioms, regional dialects and other culturally unique language skills
- Academic & professional expertise (50+ sub-tasks): Cross-disciplinary knowledge spanning liberal arts, hard sciences and specialized industry domains
3. SuperCLUE-LYB (Langya Ranking Anonymous Human Voting)
A crowdsourced blind matchup test: all model responses are anonymized, and real human raters vote to judge which output is more logical, natural and high-quality.
Combined, the three tiers deliver a complete picture: can the model chat naturally? Can it solve structured logical problems? Do end users subjectively prefer its outputs?
Its Evaluation Suite Evolves Alongside AI Technology
What makes SuperCLUE uniquely credible is its continuous iterative upgrade — it never relies on a static set of test questions, constantly expanding its evaluation dimensions to match advancing LLM capabilities.
- Mid-2023 (V1 baseline): Centered on the 10 core foundational ability sub-tasks
- H1 2024: Restructured benchmarks into three major categories: Science, Liberal Arts, and Hard Difficulty, adding rigorous precise instruction-following tests
- 2025 Major Expansion: Launched SuperCLUE-Agent dedicated to autonomous AI agent assessment, measuring three core agent competencies: tool calling, multi-step task planning, and long/short-term memory management. The same year, SuperCLUE-Science debuted to test graduate-level physics, chemistry and biology deductive reasoning.
- July 2025 Latest Benchmark: Consolidated six core vertical evaluation tracks: mathematical reasoning, scientific inference, code generation, AI Agent autonomy, precise instruction adherence, and hallucination suppression.
Its expanding testing scope mirrors the full evolution of Chinese large models: from basic conversational fluency, to rigorous logical deduction, and now autonomous task execution as AI Agents.
Key Takeaways From the 2025 Latest Benchmark Data
Global Closed-Source Top-Tier Models
OpenAI o3 leads the global overall ranking with 73.78 points, followed closely by o4-mini(high) and Gemini-2.5-Pro. Domestic leader Doubao Seed-1.6-thinking ranks 4th globally with 68.04 points.
Base Instruct Model Leaderboard
Domestic closed-source models outperform international counterparts collectively. Qwen3-235B-A22B-Instruct-2507 claims first place at 60.79 points, trailed by DeepSeek-V3-0324 and kimi-k2-0711-preview. ChatGPT-4o-latest only scores 52.46 points.
Open-Source Model Gap
Chinese open-source models hold a dominant global advantage. DeepSeek-R1-0528, Qwen3-235B-A22B-Thinking-2507 and GLM-4.5 occupy the top three open-source spots. The highest-ranked overseas open-source model scores merely 46.37 points, a nearly 20-point performance gap.
AI Agent Specialized Ranking
Doubao Seed-1.6-thinking tops the global Agent leaderboard at 90.67 points, with GLM-4.5 and SenseNovaV6 Reasoner rounding out the top three.
Persistent Global Gap in Complex Reasoning
International models retain a clear edge in advanced logical deduction: o3 and o4-mini(high) score 75.02 and 72.68 points respectively. The top domestic reasoning models DeepSeek-R1-0528 and Doubao Seed-1.6-thinking hover just above 65 points, a nearly 10-point deficit.
Straightforward Practical Guidance
Enterprise Tech Leaders Conducting Production Model Selection
SuperCLUE’s benchmark reports are an indispensable reference. Do not fixate solely on total aggregate scores — drill down into segmented sub-task metrics aligned with your business use case:
- Customer service products: Prioritize multi-turn dialogue retention and safety compliance scores
- Code assistant development: Focus on code generation and logical reasoning benchmarks
Developers & AI Researchers Tracking Chinese LLM Progress
SuperCLUE publishes monthly evaluation reports, complete with open-source GitHub project repositories and full technical documentation detailing test question design and standardized scoring logic. It serves as a reliable long-term tracking window for domestic large model technical evolution trajectories.
Casual End Users Picking Daily AI Tools
SuperCLUE’s rankings act as a filter to avoid overhyped models with inflated marketing claims that fail to deliver consistent real-world performance.
In an AI industry saturated with self-proclaimed “number one” models, independent, transparent, continuously updated third-party evaluation benchmarks are an irreplaceable public resource. Without standardized measuring tools, every vendor’s performance claim amounts to empty self-promotion.