We Compared the Top 10 LLMs — The Winner Shocked Us

In today’s fast-moving artificial intelligence landscape, large language models (LLMs) are dominating both research and enterprise agendas. These models do not simply respond to prompts, they power assistants, summarise documents, translate multilingual text, generate code, and much more. According to IBM, LLMs “are the first AI system that can handle unstructured human language at scale.”

Whether you are in IT, marketing, development or operations, understanding the top LLM models is no longer optional. At the same time, the rise of open source LLM models has dramatically altered the vendor and deployment landscape. In this blog we explore how we evaluated the top 10 LLMs, what criteria mattered most, and why the winner turned out to be surprising.

Enterprises are rapidly shifting from experimentation to production. The research firm Forrester Research, in its AI Foundation Models for Language, Q2 2024 evaluation, assessed providers across 21 criteria not just raw model performance.
In short: choosing the right LLM matters because it affects performance, cost, scalability, vendor risk, and longer-term business value. Looking only at parameter size or headline benchmarking is no longer sufficient.

We defined a rigorous evaluation framework across five key domains:

• Model Capabilities – reasoning, language understanding, multilingual ability, context length, multimodal support.
• Enterprise Readiness – support tooling, governance, vendor roadmap, maturity.
• Cost & Total Cost of Ownership – licensing, infrastructure, fine-tuning overhead, inference cost.
• Licensing & Deployment Options – proprietary vs open source, hybrid/edge deployment, flexibility.
• Ecosystem & Integration – ecosystem of tools, fine-tuning options, community, compliance frameworks.

We compared ten leading models from vendor and open-source sources. We closely considered how the open source LLM models compared to proprietary offerings in all these dimensions.

Performance & Capabilities

Many proprietary, large-parameter models delivered top performance on linguistic and reasoning tasks. However, we found that models with slightly lower reported specifications but better integration (context window, fine-tuning readiness) often out-performed in real-world enterprise use.

Licensing & Deployment Flexibility

Open-source models delivered compelling value in terms of deployment flexibility, lower licensing cost, and controllable infrastructure. As IBM points out, open-source LLM models allow organisations to “create applications … that can be customized and fine-tuned to particular use cases.”

Enterprise Readiness & Vendor Strategy

Forrester emphasises that vendor strategy, market presence and support tooling matter as much as model architecture. Many organisations ended up choosing a model not just for its raw performance, but for reliability, governance, compliance and long-term vendor strategy.

Surprise Winner: An Open-Source Model

After aggregating all criteria (not simply performance-first), our winner turned out to be a well-architected open-source model. The reason? It delivered near-top performance, had strong ecosystem support, offered deployment and licensing flexibility, and dramatically lower cost of ownership compared to the largest proprietary models.

This outcome surprised many because the assumption has been “bigger proprietary model = best”; our findings show that value-oriented enterprises can get more by balancing capabilities, cost, and deployment controllability.

• The dominance of massive proprietary models is challenged. Organisations now have viable alternatives with open-source LLM models.
• Strategic deployment matters. Choosing a model that fits your specific use case, governance model and infrastructure is more important than chasing the largest parameter count.
• Governance, monitoring, and ethics are front and centre. IBM warns that bigger is not always better—some very large models may not improve practical business outcomes.
• Total cost of ownership (TCO) should include hardware, fine-tuning, inference, updates, governance—not just licensing.
• The vendor landscape is evolving. Forrester’s evaluation shows that vendor strategy, roadmap, ecosystem and support matter as much as the model itself.

If your organisation is looking to adopt an LLM, keep these practical steps in mind:

• Define your use-case clearly. Are you summarising legal documents? Generating code? Providing multilingual customer service? The model you pick should align with the use case.
• Assess deployment options. Will you deploy on-premises, hybrid, or cloud? Open-source models may allow more control but require more infrastructure.
• Plan for governance and monitoring. LLMs can produce unpredictable output—bias, hallucinations, compliance risk. Make sure you have human-in-the-loop oversight, monitoring and auditing.
• Calculate you TCO. Don’t underestimate infrastructure, fine-tuning, inference, maintenance, and support costs.
• Vendor strategy matters. Does the model provider have staying power? Is there a roadmap? What is the ecosystem of tools? Are there enterprise-grade SLAs?
• Pilot-to-scale path. Start with a pilot, measure actual business value, workflow integration and scalability, then expand.

Selecting the right LLM is no longer about picking the largest model or chasing the highest benchmark score. Our comparison of the top 10 LLMs revealed that a carefully chosen open-source LLM model delivered the best balance of performance, cost, deployment flexibility, and enterprise readiness. As AI becomes central to business operations, organisations must pivot from “Which model is best?” to “Which model fits our business strategy, infrastructure and governance?”

In 2026 and beyond, the true winners will be those who integrate LLMs wisely, manage them responsibly, and embed them into workflows, not simply deploy a model and hope for results.

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