Augmentic BV
Haaswijkweg east 12B
3319 GC Dordrecht
The Netherlands
More and more organizations want to really push forward with AI after an initial pilot phase. They see the opportunities, have already experimented with applications, and now want to move forward structurally. But this is where it gets tricky: which AI model do you use for what? Which choice is smart, affordable and future-proof? And how do you make sure that choice fits the different roles in your organization-from finance and customer contact to development and strategy?
What you choose today determines your latitude tomorrow. One model may offer convenience, but it lacks flexibility. And if you are stuck with one supplier, you have little influence if the market or your needs change. Organizations do want that freedom. They want to decide which AI model to deploy for which task. Not every function in your organization requires the same from AI, so why use the same model everywhere?
That's why Augmentic is taking a different approach. Our platform, Augmentic_OS, makes it possible to use multiple AI models side by side, without it becoming complex. The user notices little of this: they always work the same way, with the same interface and structure. But under the hood, the platform automatically chooses the most appropriate model for the task-or leaves that up to the user or key user. Thus, the system matches different roles, processes and needs within the organization.
Access to all supported models is included in the subscription price, so you don't have to choose before a model, but can choose with a model-at the right time, for the right task. That ensures grip, flexibility and scalability. And because new models emerge at lightning speed, Augmentic_OS allows you to move with what works.
Testing, iterating and validating
In many organizations, work varies greatly by role. What a developer needs from a language model is different from what works for a policy advisor or a customer contact employee. Augmentic_OS therefore offers the possibility of not being stuck with one AI model, but choosing what works best for each task.
Starting with testing means that multiple models perform the same task-such as preparing a report, summarizing a consultation or generating a product sheet. This provides insight into differences in clarity, structure, speed and cost.
This is followed by iterate: based on feedback from the organization, settings or instructions can be adjusted. Small changes, such as an alternate prompt or a different model, often make an immediate difference-without changing the process itself.
At validate the choice is reduced to a compact set of models that have proven themselves in practice. This leaves room for experimentation, but always provides a stable and appropriate set of models for everyday use.
Longlist: LLM Capability Matrix
The matrix below is the map of models that Augmentic_OS facilitates. It is not a ranking, but a tool for targeted selection by task and role.
| Model | Location | Reasoning | Data/Analysis | Text/Language | Multimodal | Speed (🐇) | Cost (€) | Comments |
|---|---|---|---|---|---|---|---|---|
| OpenAI DALL-E 3 | EU (primary) | - | - | - | ●●●● | 🐇🐇 | €€ | Image Generation |
| OpenAI GPT-4.1 | EU (primary) | ●●● | ●●●● | ●●●● | ○ | 🐇🐇 | €€ | Balanced, reliable |
| OpenAI GPT-4.1 mini | EU (primary) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Fast, low cost |
| OpenAI GPT-4.1 nano | EU (primary) | ○ | ● | ● | ○ | 🐇🐇🐇🐇 | €€€€ | For edge/embedded |
| OpenAI GPT-4o | EU (primary) | ●●● | ●●● | ●●●● | ●●● | 🐇🐇 | €€ | Multimodal + multilingual |
| OpenAI GPT-4o mini | EU (primary) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Compact and efficient |
| OpenAI GPT-5 Thinking | EU (primary) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Complex, long reasoning |
| OpenAI GPT-5 | EU (primary) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Flagship reasoning |
| OpenAI GPT-5 mini | EU (primary) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Lightweight GPT-5 |
| OpenAI GPT-5 nano | EU (primary) | ○ | ● | ● | ○ | 🐇🐇🐇🐇 | €€€€ | Edge scenarios |
| OpenAI o1 | EU (primary) | ●●● | ●● | ●● | ○ | 🐇🐇 | €€ | Reasoning |
| OpenAI o3 | EU (primary) | ●●●● | ●●● | ●● | ○ | 🐇 | € | Advanced reasoning |
| OpenAI o3 mini / high | EU (primary) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Trade-off variants |
| OpenAI o4 mini | EU (primary) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Experimental |
| Anthropic Claude Sonnet 3.7 Reasoning | EU (primary) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Strong in structured reasoning |
| Anthropic Claude Sonnet 3.5 / 3.7 / 4 | EU/US | ●●● | ●●● | ●●● | ○ | 🐇🐇 | €€ | Long context, safe |
| Anthropic Claude Sonnet 4 Reasoning | EU (primary) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Deep reasoning |
| Google Gemini 2.5 Flash | EU (primary) | ● | ● | ●● | ●● | 🐇🐇🐇🐇 | €€€€ | Real-time |
| Google Gemini 2.5 Pro | EU (primary) | ●●● | ●●● | ●●●● | ●●● | 🐇🐇 | €€ | Strong multimodal |
| Google Gemini 2.5 Pro Reasoning | EU (primary) | ●●●● | ●●● | ●●●● | ●●● | 🐇 | € | Reasoning + multimodal |
| Mistral Large 2411 | EU (primary) | ● | ●●● | ● | ○ | 🐇🐇🐇 | €€€ | Excellent for structured tables |
| Meta Llama 3.3 70B | EU (primary) | ● | ● | ●● | ○ | 🐇🐇 | €€ | Flexible open source |
| Anthropic Claude 3.5 Haiku | US (optional) | ● | ● | ●● | ○ | 🐇🐇🐇🐇 | €€€€ | Optimized for chat |
| Anthropic Claude Opus 3 | US (optional) | ●●●● | ●●● | ●●● | ○ | 🐇 | € | Heavy reasoning |
| Amazon Nova Lite | US (optional) | ○ | ● | ● | ○ | 🐇🐇🐇 | €€€ | Cost-saving |
| Amazon Nova Pro | US (optional) | ● | ●● | ● | ○ | 🐇🐇 | €€ | Enterprise-focused |
| DeepSeek r1 | US (optional) | ● | ●●● | ● | ○ | 🐇🐇 | €€ | Strong in quantitative analysis |
Shortlist for iteration and validation
Choice is valuable; overview makes it useful. Therefore, organizations often work with a compact shortlist: small enough to move quickly, broad enough to cover key qualities (reasoning, multimodal, structure, pace, cost).
| Model | Reasoning | Data/Analysis | Text/Language | Multimodal | Speed (🐇) | Cost (€) | Why chosen |
|---|---|---|---|---|---|---|---|
| OpenAI GPT-5 (EU) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Benchmark for reasoning |
| OpenAI GPT-4.1 mini (EU) | ● | ● | ● | ○ | 🐇🐇🐇 | €€€ | Fast and cost-effective |
| Anthropic Claude Sonnet 3.7 Reasoning (EU) | ●●●● | ●●●● | ●●●● | ○ | 🐇 | € | Strong, consistent, secure |
| Google Gemini 2.5 Pro (EU) | ●●● | ●●● | ●●●● | ●●● | 🐇🐇 | €€ | Multimodal strength |
| Mistral Large 2411 (EU) | ● | ●●● | ● | ○ | 🐇🐇🐇 | €€€ | Best for structured tables/diagrams |
AI adapting to your work, not the other way around
Freedom of choice is essential if you want AI to fit the diversity of roles, tasks and processes within an organization. In Augmentic_OS, multiple models are not an exception, but the standard. The platform provides one recognizable interface and consistent way of working, regardless of which model is active under the hood. Whether analyzing, reasoning, visually summarizing or generating content-the user always works the same way. The difference is in the power behind the button: the right model is matched to the right task, keeping performance and cost in balance.
That makes Augmentic_OS flexible and future-proof. Models are easily interchangeable; the application determines the choice-not the vendor. Smart routing ensures that not every task automatically runs on the most expensive model, which in practice leads to substantial cost savings. At the same time, the way of working remains unambiguous for users: they don't have to work or learn differently. And as soon as a more powerful or smarter model is available, it can be deployed immediately-without migrations, redesigns or technical complexity. This keeps your organization in control, agile and always ready for the next step.
*Fair Use Policy
Access to all supported models is included in the subscription price. The more expensive models have reasonable usage limits per time period; in our experience, they are more than sufficient for normal use. 99% of users do not hit these limits.
