Open-source AI models can incur higher overall costs due to token inefficiency
Many people assume open-source AI models are cheaper. That’s often true, at least if you’re only looking at the per-token price. But when you zoom out and analyze how these models actually perform across typical workloads, the picture shifts.
Nous Research recently ran a study across 19 AI models and found that open-source models consumed 1.5 to 4 times more tokens than their closed-source counterparts when completing the same task. For simple knowledge queries, like asking for the capital of a country, open-source models sometimes used up to ten times more. That means it can cost more to get the same output, even if each token is technically cheaper.
Now this matters if you’re building at scale. If your team is fielding millions of AI queries per month, these inefficiencies compound quickly. You’re essentially burning compute you don’t need to, and that can hit enterprise budgets hard.
When evaluating AI tools, cost per token is no longer a sufficient metric. You need to evaluate how many tokens the model needs to complete specific real-world tasks. Without that data, you’re making budgeting decisions with partial information.
Token efficiency is a critical metric in AI deployment
Most folks benchmarking AI look at accuracy or latency. Token efficiency, how many tokens a model needs to reach a solution, doesn’t get as much attention as it should. But it’s a key piece of the equation if you care about scaling AI affordably.
Nous Research made this clear in their evaluation. They compared a wide range of models across different task types: simple knowledge questions, math problems, and logic puzzles. What they saw is that token usage varies dramatically even when output accuracy is the same. That variation directly impacts your inference costs, and it scales with usage.
If you’re a CIO or CTO looking to roll out AI tools across teams or product lines, token efficiency gives you a much clearer sense of true cost. A model that’s more accurate, but far less efficient, might cost more in the end. Knowing this upfront helps you select models that are both performance-ready and resource-smart.
Executives often ask about the “total cost of AI ownership.” Token efficiency should be a core part of that answer. It affects the economics of today and the sustainability of broader AI implementation across your stack.
Closed-source models demonstrate superior token efficiency
A lot of people still associate closed-source models with higher cost. Sure, the per-token API price is typically steeper. But Nous Research showed that when you factor in how efficiently these models use tokens, the economics change fast.
OpenAI’s o4-mini and gpt-oss models stood out in the study for being extremely efficient. For math problems, tasks that usually push models to reason step-by-step, OpenAI’s models used up to three times fewer tokens than other commercial competitors. That’s a big win in terms of both cost and speed.
Closed-source providers are clearly optimizing their architectures to minimize token usage. They’re compressing internal reasoning chains and engineering models to require fewer steps to reach conclusions. It’s an intentional design choice that improves inference efficiency at scale.
For executives, especially those deploying AI across high-volume systems, this level of optimization translates directly to lower compute costs without sacrificing accuracy. You get more performance per dollar, and you don’t need to compromise on results to get it.
Large reasoning models (LRMs) are prone to excessive token consumption even on simple tasks
Large Reasoning Models are built to think in sequences. That can help for complicated problems, but the downside is that they often don’t know when to stop. Even for basic questions, like identifying a capital city, these models can generate hundreds, sometimes thousands, of tokens reasoning through what should be a straightforward answer.
The Nous Research study highlighted this issue. These LRMs may deliver accurate responses, but they do so with excessive internal processing that drives up token usage. That significantly increases cost without any added value for simple tasks. Imagine paying a premium to get the same answer others deliver more succinctly.
There’s a practical implication here for enterprise environments. If you’re using these models for routine user queries, customer support, or any high-frequency task, you’re pulling in unnecessary overhead. It inflates your compute bill, slows down response time, and reduces efficiency across the board.
This isn’t a model flaw, it’s a design tradeoff. But it’s critical that technology leaders understand the cost behavior before designing systems around these models.
There is considerable variability in token efficiency among different open-source AI models
Open-source AI models aren’t all built the same. Some are well-optimized, others burn through tokens far too easily. That difference can have a major impact on your compute strategy and operational costs.
According to Nous Research, Nvidia’s llama-3.3-nemotron-super-49b-v1 was the most efficient open-weight model across all tested domains. It consistently used fewer tokens per task compared to its open-source peers. On the other end, newer models from companies like Mistral were flagged as outliers, consuming far more tokens than necessary to solve basic or intermediate problems.
This kind of disparity is critical when selecting a model. If you assume all open-source tools come with the same performance profile, you’re likely to make the wrong infrastructure and scaling decisions. The right model can give you both speed and affordability. The wrong one can drain your compute budget, quickly.
For executives overseeing AI adoption, these findings show that benchmarking token efficiency is a practical requirement, not a technical detail. You need to know which models deliver performance without excessive resource usage, especially when deploying AI across multiple departments or customer-facing services.
Efficiency optimization remains a strategic focus for closed-source AI providers
Closed-source AI companies are not just prioritizing accuracy, they’re actively optimizing for lower resource use. That strategic focus is showing clear results in comparative studies.
Nous Research found that closed models have seen steady improvements in how they compress and streamline reasoning processes. These providers are reinventing how their models approach step-by-step problem solving to reduce the number of tokens needed. Even though their per-token price remains higher than open alternatives, these efficiency gains are shifting the overall value equation.
What’s happening here goes beyond improving user-facing outputs. It’s architectural. These companies are designing models that do more with less data and fewer computational cycles. As a result, total inference cost drops, and businesses deploying them at scale reap the benefit.
This should matter to any leader weighing whether to license closed-source tools or build with open infrastructure. Efficiency is now a competitive differentiator. Paying more per token doesn’t necessarily mean spending more overall.
Measuring token efficiency presents unique methodological challenges
Measuring token efficiency isn’t straightforward, especially when you’re dealing with closed-source models. These models often don’t show their internal reasoning processes. Instead, they generate compressed summaries, shorter, cleaned-up representations of the reasoning chain, that make it harder to evaluate how much actual computation took place behind the output.
The research team at Nous addressed this gap by using completion tokens, a count of the total tokens billed per query, as a proxy for reasoning effort. It’s not a perfect solution, but it’s currently one of the most reliable indicators available. They also adjusted standard problem sets by modifying commonly known tasks, such as altering math competition questions, to ensure models weren’t simply recalling memorized answers.
This approach allowed them to compare token usage more precisely across both open and closed AI systems. Without it, the market’s understanding of efficiency would be skewed. Closed models might appear more efficient simply because we’re measuring incomplete outputs. With token completion data, we start to see a clearer, more objective picture.
For leaders making investment decisions, this is a reminder that evaluating AI tools requires more than reading vendor claims. Consistent and transparent benchmarking practices are necessary to inform real cost-performance analysis, and that’s especially true as these tools become more embedded in mission-critical systems.
Token efficiency is emerging as a key competitive advantage in the AI landscape
When AI models compete, it’s no longer just about intelligence, it’s about efficiency. OpenAI’s latest gpt-oss models are a good example of this shift. They deliver high performance at state-of-the-art efficiency levels, and importantly, they make their reasoning processes more transparent and accessible for further optimization.
Nous Research’s findings point to a direction the industry is already following: AI models must balance accuracy with resource use, and the ones that do both well will dominate. Token efficiency isn’t just a technical metric anymore. It’s beginning to shape priorities in product roadmaps and influence enterprise procurement decisions.
For C-suite leaders, this signals a pivot in the AI maturity curve. As organizations go from experiments to enterprise-grade deployments, cost predictability and operational efficiency move to the forefront. Models that can perform well under strict efficiency constraints will offer better ROI, scale more sustainably, and integrate more seamlessly into product or data pipelines.
In conclusion
If you’re leading AI strategy, token efficiency should already be on your radar. It’s not just a technical detail, it’s a cost driver, a performance lever, and soon, a competitive benchmark. The assumption that open-source equals cheaper is no longer reliable. Some open models work lean, but many burn through tokens fast, pushing up real-world compute costs.
Closed-source providers, especially those like OpenAI, are actively prioritizing efficiency. That translates to predictable performance at scale and stronger economics over time, without compromising output quality.
As the AI landscape matures, your models will need to do more with less. This shift isn’t about spending more, it’s about spending smarter. Leaner inference, better design, and optimized reasoning aren’t just engineering goals, they’re business strategy.
