GDPval benchmark leaderboard (2026): best LLMs for reasoning

Ever wonder which AI model is best at general reasoning?

The GDPval benchmark compares how well AI models perform on real-world reasoning and knowledge tasks.

Unlike benchmarks such as MMLU, which focus on exam-style multiple-choice questions, GDPval evaluates how models handle broader analytical work tasks across domains.

As AI systems are increasingly used for research, analysis, and decision support, benchmarks like GDPval provide a useful snapshot of how frontier models compare in real-world reasoning.

Benchmark data last checked: March 2026

Why GDPval benchmark matters

GDPval is not just a trivia benchmark. It is a useful way to compare the best AI models for general reasoning.

For GDPval, it is one of the best proxies we have for:

• multi-domain reasoning and knowledge synthesis

• accuracy across complex analytical questions

• consistency across technical and non-technical domains

So if your work touches research, analysis, knowledge work, or internal decision support, these scores are a useful signal when choosing an AI model.

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GDPval leaderboard: best LLMs for reasoning in 2026

GDPval shows something interesting: the top two models are extremely close, while the performance gap grows quickly after that.

the top tier holds extremely tight

• GPT-5.4 leads with 1667

• Claude Sonnet 4.6 follows at 1633

These two models sit in their own performance bracket. In practical terms, they tend to produce more reliable reasoning chains, fewer hallucinations, and stronger answers across diverse domains.

For complex business analysis or technical knowledge tasks, this tier usually requires the least supervision.

the middle tier is capable but clearly behind

Gemini 3.1 Pro scores 1315 and Qwen 3.5 scores around 1216.

These models still perform well for many workflows. But compared to the leaders, they may require:

• more prompt guidance

• more fact checking

• more retries on complex reasoning tasks

For many organizations, this tier can still deliver solid results, especially when cost or speed matters more than absolute accuracy.

the lower tier becomes a different category

Llama 4 Maverick scores 471

That is more than 1,100 points behind the leader.

This suggests a large gap in the types of reasoning GDPval measures.

It does not mean the model is useless. It simply means it struggles with the benchmark’s mix of complex analytical and knowledge-heavy tasks.

What is the GDPval benchmark?

GDPval is a leaderboard-style benchmark designed to compare how well frontier AI models perform on broad knowledge and reasoning tasks.

Models are evaluated on a large set of questions that require understanding context, analyzing information, and selecting the most accurate answer.

The final score reflects a model’s overall performance across this diverse question set, which is why GDPval is often used as a quick signal of general reasoning ability.

The test includes hundreds of evaluation questions across:

• analytical reasoning

• general knowledge and domain expertise

• applied problem solving

Because the dataset spans multiple fields, models must demonstrate both factual knowledge and reasoning ability to perform well.

Top frontier models now perform far above typical human baseline scores on many individual question sets, although performance still varies widely by domain.

This variation is one reason benchmarks like GDPval are still useful for comparing model capability.

What makes the GDPval benchmark different?

GDPval focuses on cross-domain evaluation rather than a single specialized skill.

Many AI benchmarks measure narrow abilities such as coding, math, or image generation. GDPval instead evaluates how consistently a model performs across a broad spectrum of tasks.

The dataset combines questions from multiple academic and technical areas, making it closer to how AI systems are actually used in research, business analysis, and decision support.

Because of that breadth, GDPval tends to highlight which models are strongest at general reasoning, not just narrow benchmark optimization.

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