20 LLM Benchmarks That Still Matter
Trust in traditional LLM benchmarks is understandably waning, as several inherent issues have become increasingly apparent to AI practitioners. More than one guest on ODSC’s podcast has branded them “effectively useless” as that often masks significant deficiencies in the LLMs
Drawbacks of traditional LLM benchmarks range from training data leakage (benchmarks inadvertently overlap with a model’s training set) to overfitting. AI practitioners complain that while LLMs perform well on benchmarks in limited contexts, they often struggle in complex real-world scenarios, and the benchmarks themselves fail to effectively measure nuanced reasoning
However, new LLM benchmarks are coming online that are good at measuring an LLM’s complex reasoning and contextual understanding beyond simplistic pattern matching. Despite rapid model evolution outpacing existing benchmarks, we believe this next generation will keep up.
Solved LLM Benchmarks?
Several traditional LLM benchmarks can be considered effectively “solved” by recent advancements, limiting their usefulness as indicators of progress. For instance, datasets like the Stanford Question Answering Dataset (SQuAD) and GLUE (General Language Understanding Evaluation) have seen top-performing LLMs surpass human-level performance, making it increasingly difficult for these benchmarks to distinguish between newer models.
Even the Winograd Schema Challenge, originally designed to test common sense reasoning, has been largely mastered by leveraging vast training datasets, diminishing its effectiveness. Similarly, SuperGLUE, created as a more challenging successor to GLUE, has seen LLMs consistently perform at impressive levels, often reaching or exceeding human baselines.
Other benchmarks, such as HellaSwag — intended to be adversarially difficult — are now routinely tackled by state-of-the-art models. Likewise, benchmarks focused on factual recall, like RACE (Reading Comprehension from Examinations) and TriviaQA, have been outpaced by the sheer scale and training capabilities of modern LLMs. Even tasks like MNLI (Multi-Genre Natural Language Inference), once a staple for evaluating textual entailment, now see consistently high scores from leading models.
While these benchmarks remain useful for establishing baseline performance, they have become somewhat redundant in measuring the nuanced reasoning, adaptability, and real-world application capabilities of advanced LLMs. This underscores the need for new, more robust metrics that can better capture the complexities of next-generation models.
LLM Benchmarks That Still Matter
So which LLM benchmarks still matter, and are there newer ones? These benchmarks remain challenging for LLMs because they demand more than just language understanding — they require advanced reasoning, alignment, discrete decision-making, deep subject matter expertise, and the capacity to manage biases effectively. We’ve grouped them here by category:
Reasoning and Problem-Solving Benchmarks
- BIG-bench (Beyond the Imitation Game Benchmark): Evaluates a broad range of reasoning tasks, including general intelligence, creativity, and logic beyond pattern recognition. Its diverse set of tasks makes it a comprehensive challenge for LLMs.
- ARC (AI2 Reasoning Challenge): Focuses on reasoning and applying background knowledge to answer grade-school science questions. It requires synthesizing information and reasoning, not just factual recall.
- OpenBookQA: Tests reasoning over a small set of core science facts, requiring the model to connect information meaningfully rather than relying on memorization.
- WinoGrande: Requires commonsense reasoning to resolve ambiguities in language, focusing on subtle contextual clues. This makes it particularly challenging for LLMs lacking nuanced contextual understanding.
- DROP (Discrete Reasoning Over Paragraphs): Involves numerical reasoning, discrete operations like counting or sorting, and multi-step comprehension. These tasks require logical reasoning across multiple steps.
- GSM8K (Grade School Math 8K): Focuses on solving grade-school-level math problems with step-by-step reasoning, emphasizing coherent logical progression.
- TruthfulQA: Measures reasoning accuracy, particularly in resisting misleading questions. It challenges models to avoid plausible-sounding but incorrect responses.
Knowledge and Comprehension LLM Benchmarks
- MMLU (Massive Multitask Language Understanding): Tests deep knowledge across a wide range of disciplines, from humanities to engineering. It challenges models to recall and apply specialized information effectively, providing insights into their breadth of understanding.
Numerical and Math Reasoning Benchmarks
- DROP: Detailed above and also fits here
- GSM8K (Grade School Math 8K): Specifically focuses on math problems, requiring logical steps and calculations, highlighting difficulties in systematic problem-solving.
Truthfulness and Bias Benchmarks
- TruthfulQA: Detailed above and also fits here
- BBQ (Bias Benchmark for Question-Answering): Evaluates the model’s susceptibility to biases embedded in training data and its ability to generate unbiased, fair responses.
Efficiency and Cost-Related Benchmarks: A Practical Perspective
As LLMs continue to grow in complexity, both efficiency and cost-related benchmarks have become essential for assessing their practicality in real-world deployment. These benchmarks go beyond measuring performance accuracy to evaluate how efficiently a model operates and the resources required for its use.
Token Efficiency Benchmarks focus on the number of tokens used to complete tasks, offering insights into how well a model generates concise and relevant responses. Metrics like tokens per response and context compression are critical for optimizing API costs, particularly in scenarios where token usage directly impacts pricing. Tools like OpenAI API reports, Hugging Face, and LangChain help developers monitor and minimize token usage.
Cost Benchmarks evaluate the computational expense of training and deploying models. Metrics like floating-point operations (FLOPs), analyzed through platforms like MCommons’s MLPerf, provide insights into the trade-offs between accuracy and computational intensity. Additionally, cost comparisons from sources like Vellum, and Hugging Face TCO Comparison Calculator highlight the financial implications of deploying LLMs at scale. The increasing focus on sustainability also brings energy usage and carbon footprint metrics, such as those provided by Carbontracker, to the forefront, helping assess the environmental impact of model usage.
Latency and Real-Time Performance Benchmarks are critical for applications requiring immediate responses, such as chatbots or recommendation systems. Metrics like inference speed (available via Hugging Face) MLPerf and continuous batching ensure models can scale effectively without sacrificing responsiveness.
Finally, Scalability and Practical Deployment Benchmarks assess how well an LLM handles high-traffic environments. Benchmarks like NVIDIA TensorRT evaluate GPU-based optimizations, while memory footprint comparisons determine whether a model can operate efficiently within existing hardware constraints.
Conclusion
In summary, while many traditional benchmarks may be considered somewhat “solved,” new metrics focusing on reasoning, efficiency, and real-world applicability are emerging to better evaluate LLMs. These next-generation benchmarks will guide the development of models that are not only powerful but also practical and responsible for real-world deployment.
Stay Ahead with LLM Benchmarks
To truly stay ahead in the rapidly evolving AI landscape, it’s essential to learn from the experts shaping the next wave of innovation. At ODSC East 2025 this May 13th-15th and our virtual month-long AI Builders Summit starting in January, you’ll dive deep into the latest benchmarks, techniques, and tools that define state-of-the-art LLM development. From hands-on workshops to insights from top practitioners, these events are your gateway to building impactful, scalable, and efficient AI solutions.
Don’t miss your chance to connect with the brightest minds in AI. Register now for ODSC East 2025 or join the AI Builders Summit and take your skills to the next level!
