Which AI model to choosefor an enterprise project?
Our dated and assumed ranking of major language models: what we actually deploy for our clients, and why. Not another benchmark—an agency perspective, focused on cost, latency, and compliance.
Our tier list of AI models.
One criterion: the value of a model in a production enterprise project. Reasoning ability, cost per million tokens, latency, API stability, and data hosting location.
What we deploy in production without hesitation
The models we choose when reasoning quality takes precedence over cost: tool-equipped agents, complex corpus analysis, code generation.
- Claude Fable 5 — A step above for very long reasoning. The extra cost is only justified for a minority of tasks—rarely at scale.
- GPT-5.6 Sol — OpenAI’s premium offering. The broadest ecosystem of tools and libraries, very strong on code.
The best quality-to-price ratio
What we deploy for most projects: good enough for almost everything, cheap enough to run continuously without breaking the bank.
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- Claude Opus 5 — Very strong on tool-using agents and long-running tasks, with stable behaviour from one version to the next. Fable now beats it when reasoning quality is the priority, but it remains an excellent default on most projects.
- Gemini 3.7 Flash — Extremely fast and very affordable. Ideal for high-volume document processing and batch jobs.
- Mistral Medium 3.5 — Our go-to when data must stay in Europe. More than sufficient for document RAG, with explicit EU hosting.
Excellent for specific use cases
Excellent in their niche, less versatile. We use them when the use case justifies it—not by default.
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- Claude Sonnet 5 — The right choice for assistants and high-volume RAG, where its latency and price make the difference. On complex reasoning, the models in the tiers above take the lead.
- GPT-5.6 Luna — Very high value for money and good execution speed. An excellent candidate for targeted, high-volume use cases, especially if your stack already runs on OpenAI.
- Grok 4.6 — High performance-to-cost ratio. Best for use cases where data sensitivity is not a concern.
- Qwen3.8-Max — Very strong in multilingual tasks. Part of the range is self-hostable, making it a credible fallback option.
- Mistral Small — Open weights, runs on your own infrastructure. The choice when nothing must leave the network.
Reliable for self-hosting
Freely downloadable, to be self-hosted. Inference costs are controlled, but operational costs are real—GPUs, monitoring, updates.
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- DeepSeek-V4-Pro — Excellent capacity-to-cost ratio for reasoning. Sovereign hosting is a requirement here, not an option.
- GLM-5.2 Turbo — Fast and cost-effective. Ideal for high-volume structured tasks, less so for open-ended reasoning.
To watch, not yet validated by us
Promising models we haven’t yet deployed on a client project. We don’t recommend them until we have.
- Kimi K3 — Reported strong performance on long contexts. Needs validation on real-world corpora before any recommendation.
This ranking is an agency opinion, not a benchmark. It evolves quickly: a model released last week could reshuffle the deck. We revise it with every significant update and revalidate it against our clients’ real-world use cases before making recommendations.
Five criteria, no overall score.
No model is ever "the best" in absolute terms. Here’s what we look at before deciding, in this order.
Reasoning quality for YOUR use case
Public rankings measure generic tasks. We replay your own cases—your documents, your business rules, your output formats—across three or four models before deciding. It’s the only benchmark that commits someone.
Cost per million tokens
An internal assistant running all day costs ten to thirty times more on a top-tier model than on a mid-range one, with often no visible gain for the end user. The right approach: start high to validate feasibility, then step down until quality drops.
Perceived latency
In a chat, two extra seconds kill the experience. In overnight processing, no one notices. The right model depends first on whether someone is waiting in front of their screen.
Data hosting location
Healthcare, public sector, legal, defense: the question isn’t 'which is the best model' but 'which ones am I allowed to use.' This is decided upfront, not at the end—it’s what eliminates most candidates.
Long-term stability
A model deprecated six months after deployment means unbudgeted rework. We favor providers that announce their lifecycle, and we keep the application able to switch models without rewriting.
The model is never the point.
Choosing the model takes one meeting; the rest takes the project. Here’s what actually determines the success of an AI use case.
Frame before choosing
Model selection comes at the end of scoping, not the beginning. First, define the use case, available data, and acceptable risk level—the model is just a fine-tuning variable.
AI scopingAn assistant connected to your data
The quality of a corporate assistant depends first on how it accesses your documents, and only then on the chosen model. That’s where the real difference lies.
AI assistants on your dataStay sovereign over your data
Hosting in Europe, open-weight models, deployment on your own infrastructure: when data cannot leave, the selection narrows—but remains more than sufficient.
Sovereign applicationsPut a model into your processes
A model only delivers value once integrated with your tools: CRM, ERP, email, DMS. This is where a small, fast model often beats a top-tier one.
AutomationsYour questions, our answers.
The questions that come up whenever we discuss model selection with an IT department or business leadership.
An AI use case to decide, an assistant to connect to your data, a model choice to document? Let’s talk data, budget, and real constraints.
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