In the early days of generative AI being publicly available, I was on a committee within a large enterprise that monitored the direction of AI development within the organization. One day, one of my colleagues announced that he had built a small language model that ran on his laptop. I asked, is it any good? He replied, good for what? I had no answer, and that was an AHA! moment for me.<\/p>\n\n\n\n
Asking which is the best LLM is like asking which is the best pizza. The answer will depend on any number of criteria and can change from day to day. People can be very passionate about their choice. The \u201cbest LLM\u201d is the wrong question. The value isn\u2019t simply the model, but the surrounding factors as well. In fact, the model itself is rarely the decisive factor.<\/p>\n\n\n\n
Let\u2019s start by changing that question. Instead of, what is the best LLM, ask yourself, best for what? Best for task performance? But what makes it best? Speed, accuracy, lack of hallucinations, following instructions, or explaining its reasoning? What is the precise task or tasks you plan to accomplish?
What about security, privacy, and compliance? Key considerations are data residency, regulatory frameworks (think PIPEDA or GDPR, HIPAA, etc.), zero-retention or no-training guarantees, private vs. public deployment, auditability and logging, as well as model transparency and interpretation of results. A model that may be considered \u201cbest\u201d for a particular task may be unusable if it can\u2019t meet your security requirements.
Then there\u2019s cultural fit. LLMs will give different results based on the data used to train them. For example, a Canadian-trained LLM may understand Canadian idioms best, while a Japanese-trained LLM will likely have a better grasp of Japanese business etiquette. LLMs \u2013 and therefore any AI functionality based on those LLMs \u2013 reflect the geopolitics and biases of the data used for training. This applies to languages as well: Canadian French and France French are as different as German from Germany and Swiss German.
In addition to reflecting your cultural norms, there\u2019s a legitimate feel-good factor in supporting your local businesses and ecosystems. If I\u2019m Canadian, I want to support Canadian. I have a chance to support Canadian Indigenous? Even better. Replace Canada with your own location \u2013 we all want our countries or regions to thrive.
Ease of implementation cannot be overlooked. Sometimes the \u201cbest\u201d model is the one that can fit within your technology stack with the least friction. This can be based on the availability of API endpoints that suit your needs, the infrastructure required to support the model, the latency and throughput, and even your team\u2019s knowledge and experience. A model that fits in well and easily may be more valuable than a model that rates higher in another category but is challenging to implement or support.
Coming back to the point of what the LLM is to be used for, perhaps you need it to be customized. This could mean fine-tuning, domain adaptation, flexibility with prompt engineering, or the ability to set and monitor guardrails. Some organizations want highly constrained models that avoid risk, others want more open models with more adaptability. Choosing open source is usually more adaptable but comes without the enterprise support or security testing that goes into closed source LLMs. The best LLM for a specific use case may not work for another of your use cases. Look towards your future as well as the current needs.
Let\u2019s not leave out cost. As much as we all want the best of the best, what we can afford is a different question. Some models are extremely powerful but expensive to run at scale, some are smaller but are optimized for speed and cost. Some models can be quantized and run cheaply on hardware you already own, some require infrastructure that will cost you more than the model itself. Performance-per-dollar matters, but needs to be evaluated against the benefits you\u2019ll see, as well as the simple question of, how much money do you have to spend?<\/p>\n\n\n\n
Each of the above constraints alone can disqualify a model. Together, they make the idea of a \u201cbest\u201d LLM meaningless.
What all these considerations have in common is that they are not properties of the model alone. They\u2019re dependent on how the model is selected, governed, and integrated into your existing stack. This is why leading organizations are decoupling AI capability from model selection, and investing in platforms that abstract the functionality, security, and governance away from the model itself.
This is all well and good, but LLMs are benchmarked. You can find reports and comparisons, academic papers and other reliable sources. You know what you need your LLM for and you\u2019re going to pick the best at it, whether \u201cit\u201d is reasoning, coding, math, multilingual ability, knowledge recall, or other dimensions. Great!
Well\u2026 not so great. There are continual announcements of new models, new releases, new versions of existing models, so the benchmarks and comparisons are a constantly moving target. OpenAI alone had 19 releases in 2025 and are on target to match that in 2026. Anthropic, Google, Meta, Microsoft, Mistral, X, Cohere, DeepSeek\u2026 the list goes on. You can choose the \u201cbest\u201d model, and by the time you\u2019ve implemented it, another LLM tops the ranks.<\/p>\n\n\n\n
So, you\u2019re asking, what do we do? So many choices, so much to consider, and the rate of change is so fast. These aren\u2019t separate concerns \u2013 they\u2019re all symptoms of the same underlying truth: models are changing faster than organizations can evaluate, acquire, or deploy them.
The answer: plug-and-play architecture. Your AI platforms and tooling need to work with YOUR selected LLM, rather than your organization having to adapt and add extra costs, security, workflows or guardrails to accommodate an LLM that isn\u2019t right for you. What is required for a regulated financial institution is going to be different from a multilingual NGO will be different from a global enterprise facing data residency constraints. When your needs change \u2013 new use cases, new governance, new budget, new LLM capabilities \u2013 you should be able to swap your LLM smoothly and seamlessly. The easiest changes to manage are those that bring value without the disruption.
In a world where LLM rankings change faster than procurement cycles, the winning strategy isn\u2019t picking the \u201cbest\u201d model. It\u2019s refusing to be locked into a particular vendor or model.
If you want the freedom to adopt the best LLM for your purpose today without being trapped in the future, contact us at info@licorp.ai to learn more about how Fluent can be deployed as a secure, turnkey solution independently within your selected technology stack.<\/p>\n\n\n\n
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In the early days of generative AI being publicly available, I was on a committee within a large enterprise that monitored the direction of AI development within the organization. One day, one of my colleagues announced that he had built a small language model that ran on his laptop. I asked, is it any good? […]<\/p>\n","protected":false},"author":3,"featured_media":9,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-7","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"\n