The popularity of large language models has led to a wave of benchmarks, evaluations, and leaderboards. One that’s grown in influence is the Open LLM Leaderboard, which ranks open-source models based on their performance across a range of tasks. But there’s a growing discussion that goes beyond accuracy and benchmark scores: how much energy are these models consuming, and what are the resulting CO₂ emissions?

It’s not just about how smart a model is anymore—it’s also about how efficient and environmentally responsible it is. This article looks at both sides of the conversation: performance and emissions.

Understanding Model Performance in Context

The Open LLM Leaderboard evaluates open-source language models using multiple benchmarks such as MMLU, ARC, HellaSwag, TruthfulQA, and others. These test reasoning, general knowledge, factual correctness, and consistency. A higher score on the leaderboard often signals a model that can hold up well in applications like coding help, tutoring, or summarizing content. However, achieving these scores requires significant computational effort, especially during the pretraining and fine-tuning phases. Larger model sizes tend to perform better but often demand more computing, which translates to more power usage and higher emissions.

When comparing model performance, it's useful to look at parameter size, training data volume, and training method (e.g., supervised fine-tuning or reinforcement learning). A 13B parameter model might outperform a smaller one, but if it consumes 10x the energy for a marginal performance boost, the trade-off starts to look questionable—especially when scaled up to real-world usage. Performance should be weighed not only in terms of output quality but also in terms of training and inference costs.

Some models on the leaderboard perform surprisingly well given their smaller size, showing that efficient architecture and well-chosen training data can go a long way. Developers are increasingly paying attention to these “efficiency-per-point” ratios as they try to balance competitive scores with manageable resource usage.

Carbon Emissions: The Invisible Cost

Most leaderboard comparisons rarely mention the carbon footprint of the models involved. Yet, training a single large language model can generate several tons of CO₂ emissions. Factors influencing emissions include the number of GPUs used, total training time, hardware efficiency, and even the data center's location since electricity sources vary in their carbon intensity.

Training isn’t the only phase where emissions pile up. Once deployed, these models serve millions of queries daily, which involves ongoing computation and cooling. This is especially true for popular models with high throughput, such as chat assistants or embedded AI in productivity tools. Inference might seem lighter than training, but its frequency makes it a major contributor to long-term emissions.

Some institutions and research labs now report energy usage and carbon emissions along with model benchmarks. This is still far from standard practice, but it marks a shift toward transparency. There's growing interest in estimating emissions per inference and benchmark point, which would make it easier to compare models more fairly. This information could help users and developers make decisions that take into account both performance and sustainability.

Model Efficiency and Trade-Offs

When a model ranks highly on the Open LLM Leaderboard, it sends a signal of capability. However, there is increasing attention to the cost of that performance in terms of energy. Some models are optimized for efficiency through approaches such as quantization, pruning, or distilled training, which make them faster and less resource-intensive while preserving most of their capabilities. These models often trade a few points of performance for dramatic reductions in emissions and latency.

This raises a key question: Do we always need the highest-performing model for every use case? In many applications, such as customer support or document summarization, near-top-tier performance is more than sufficient—especially if the model is cheaper to run and less demanding on infrastructure. Some smaller models now come close to GPT-class capabilities but require a fraction of the energy. These efficient alternatives are especially useful for organizations that want to reduce environmental impact or cut cloud costs.

The leaderboard doesn’t yet show energy or emissions data side by side with accuracy scores, but it’s becoming clear that efficiency should play a bigger role in how we think about AI quality. A model that ranks third with 50% less energy use might be more suitable than the one at the top of the board.

The Road Ahead: Transparency and Responsibility

There's still no standard for reporting CO₂ emissions in AI model development, but some efforts are underway. Projects such as the AI Index, ML CO2 Impact, and CodeCarbon aim to provide tools and frameworks for tracking emissions. If integrated into leaderboards, these could shift the way models are evaluated and presented to the public.

The Open LLM Leaderboard has set a strong precedent by encouraging openness in model capabilities. Extending that transparency to cover emissions would make it a fuller picture of model trade-offs. For developers and researchers, knowing the carbon cost of training and deploying a model is becoming just as relevant as knowing its MMLU or TruthfulQA scores.

There is also an opportunity for the community to agree on guidelines for efficient AI. For instance, including per-inference energy costs or emissions per training run alongside model descriptions could help users make more informed decisions. Some institutions already choose models based on power efficiency, especially when operating under strict hardware or regulatory constraints.

In the long run, models that perform well while keeping emissions low will be more than just efficient—they'll be sustainable. And that’s a direction the field seems ready to embrace.

Conclusion

Balancing model performance with environmental responsibility is becoming more urgent as language models grow larger and more widely used. The Open LLM Leaderboard highlights capabilities, but it doesn’t yet reflect the energy or emissions involved. As interest in sustainable AI grows, the need for transparency around carbon impact will shape how models are built, selected, and deployed. Prioritizing efficiency alongside accuracy can lead to more thoughtful choices, especially in real-world applications. Tracking emissions isn’t just an ethical move—it's practical. With better tools and more awareness, developers can aim for models that perform well without a high environmental cost.