From this article onward, we move up one level.
The previous two articles covered how to express a request clearly to an AI, which is a basic skill everyone needs. This article covers a different question: which AI should be given the task?
The first half of this article, the cheat sheet, is useful to individual users and to decision makers alike. Knowing which model to use for research and which to use for code benefits everyone.
The second half, covering ten selection criteria and multi-model governance, is reference material for teams and companies that are selecting models or designing AI agents. If you are an individual user who wants to get started quickly, you may skip that section and return to it when you need it.
When a company introduces generative AI, the first challenge is usually selecting the most suitable LLM. The first question that follows is always the same: should one LLM handle everything, or should different models be selected for different tasks?
After studying how the major LLMs are trained, how they are positioned, and how they handle compliance, and after considering what companies need in terms of productivity, governance, and security during an AI transition, we designed a platform to help companies select, apply, and manage LLMs. In short, we believe companies should be able to choose between a single model and multi-model collaboration. They should not be limited to one model, nor prevented from using several.
Because each LLM differs in training data, guardrail configuration, and user data protection policy, they display distinctly different values, response styles, and risk profiles. Relying on a single model provides consistency, but leaves you exposed to that model’s implicit biases and information limits. Collaboration between different models allows perspectives to be compared and cross-checked, which lowers risk and opens up more applications. Most importantly, whether you use one model or several, the result must align with company policy and be effectively supervised. This is one of the core principles of the AnyInsight.ai platform. Conversely, using several models without a single platform to align and manage them turns the advantages of a multi-model approach into inconsistent policies and fragmented data protection.
Looking at the risks of relying on a single LLM
Having one LLM handle every task is comparable to using a single telecommunications vendor for all of your internet access, telephony, internal network, security, cloud infrastructure, and customer service applications. Management policy is unified and support is simple, but when the vendor has an outage, or when one of its services is not competitive, the entire operation stalls or requires continual compromise. Dividing the same services into three supply categories — external network, internal network, and security — and selecting the best vendor for each produces greater combined benefit and considerably improves operational resilience.
| Perspective | The risk of a single LLM | The value of multi-model collaboration |
|---|---|---|
| Business continuity | A change in features, a service interruption, or a change in service policy becomes a single point of failure. You are often obliged to accept the cost of an upgrade or the risk of reduced functionality. | Critical processes and core applications can be matched to the most suitable LLM, and you can switch quickly or migrate gradually, which raises overall performance. |
| Pace of innovation | Models are upgraded on different schedules. Some release new capabilities frequently, others update every few months. Committing to one model limits application development and service expansion. | You can adopt each vendor’s leading capabilities as they appear, such as tool use, longer context, or mixed vision, which produces a flexible and modular upgrade path. |
| Risk hedging and governance | If model bias or hallucination appears in a core application, there is no second opinion available. When a compliance policy changes, the entire system has to be revalidated. | A cross-check mechanism reduces deviation and intercepts high-risk answers. Tasks can also be assigned to different models according to sensitivity. |
Cheat sheet, part one: what each of the seven models is best at
Before looking at individual models, one point runs through all of them. Almost every recent third-party evaluation reaches the same conclusion: there is no single strongest model, and the correct approach is to assign each task to the model best suited to it. Which model is best depends on your task, your budget, your latency requirements, and your data privacy requirements. For most organizations, routing different tasks to different models produces better results than committing to one.
The seven model families currently supported on AnyInsight.ai are listed below, with the positioning that has remained relatively stable for each. Positioning changes as versions and strategies change.
| Model (developer) | Core positioning and strengths | Best-suited tasks | An honest caveat |
|---|---|---|---|
| ChatGPT (OpenAI) | The most mature ecosystem and the widest tool integration. A generalist. Natively multimodal, handling text, image, audio, and video in one model. Fast, with strong agent and tool integration. | General conversation, cross-modal tasks, and automated workflows that need immediate response or connection to external tools | Capable across the board, but not necessarily first in any single area. A safe default rather than the leader in every domain. |
| Gemini (Google) | Very long context, up to one million tokens. Strong scientific reasoning and multimodality. Deeply integrated with the Google ecosystem, including Search, Workspace, and Android. A generous free tier. | Summarizing entire manuals or large documents, long-context and high-volume tasks, multimodal retrieval-augmented generation | Works best inside the Google ecosystem. The advantage is reduced outside it. |
| Claude (Anthropic) | Leading coding ability and long-context reasoning. Solid writing and logical analysis. Rigorous safety alignment. | Software development and code review, in-depth analysis of complex documents, writing that requires careful reasoning and high-quality long form | Stricter guardrails. More conservative with some borderline content. |
| Perplexity | Focused on being an answer engine. Searches the live web and attaches a source to each item. Built around search, synthesis, and verification. | Real-time research, fact checking, competitive and market intelligence, summaries where every source must be clickable and verifiable | Weaker at long-form writing, deep reasoning, and coding. Use it as a research tool rather than as the main writing or analysis model. |
| Grok (xAI) | Exclusive real-time access to the X (formerly Twitter) data stream. The most sensitive to breaking news, social sentiment, and current events. Includes reasoning and deep search. A bolder style. | Tracking current events, social sentiment and trend analysis, and any task that asks what is happening right now | Still behind Claude and ChatGPT on coding and some other tasks. Tone and content policy are more permissive. |
| Mistral (France / EU) | Open weights combined with European data sovereignty. Can be deployed on-premises. Complies with GDPR and the EU AI Act. Particularly strong in European languages such as French, German, Spanish, and Italian. Cost efficient. | Situations that require data residency, self-hosting, or regulatory constraint; content in European languages | The value is in sovereignty, openness, and control rather than in peak performance. |
| TAIDE (Taiwan) | Taiwan’s government-backed Traditional Chinese model, built around Taiwanese culture, usage, and local conditions, with an emphasis on trustworthiness. A useful example of a sovereign national LLM. | Traditional Chinese writing such as letters, summaries, and articles; Chinese-English translation; Taiwan-specific context and regulation; countering misinformation | A small model. General capability is below frontier level. It is best used with fine-tuning on your own data or with retrieval-augmented generation. |
Information updated July 2026. Model versions are updated every one or two months. The table describes the relatively stable positioning and strengths of each vendor rather than benchmark scores for a specific version. We recommend reviewing it periodically.
Cheat sheet, part two: reading model names, from large to small
Every vendor offers a family of models rather than a single product. Reading a model name requires only two axes:
- Axis one: size or tier. This determines how capable, how expensive, and how slow the model is.
- Axis two: version number. A higher number usually indicates a newer generation.
The key principle: the larger the name or tier, the stronger the reasoning, but also the higher the cost and the slower the response. The smaller the tier, the faster and cheaper. Each vendor uses different words to indicate size, as follows:
| Brand | How to read size and capability | How to read the generation | Worked example |
|---|---|---|---|
| ChatGPT | mini or nano means small, fast, and inexpensive. No suffix means the standard version. | Higher is newer (5.4 to 5.5 to 5.6) | “GPT-5.4 mini” is the lightweight version of generation 5.4 |
| Gemini | Ultra > Pro > Flash > Flash-Lite. Pro is capable; Flash is fast and inexpensive. | Higher is newer (2.5 to 3 to 3.5) | “Gemini 3.5 Flash” is generation 3.5, optimized for speed and cost |
| Claude | Opus > Sonnet > Haiku. A memory aid: the shorter the poem, the smaller and faster the model. A haiku is the shortest, so Haiku is the lightest. | Higher is newer (4.6 to 4.8 to 5) | “Claude Opus 4.8” is the most capable tier, version 4.8 |
| Perplexity | An exception. What varies is search depth rather than model size: Web, then Pro Search, then Research, each more thorough than the last. | Depends on the underlying model. Paid plans also allow you to specify it. | “Research mode” is the most thorough research level |
| Grok | Heavy (highest compute) > standard > Mini (lightweight) | Higher is newer (3 to 4 to 4.3) | “Grok 4 Heavy” is generation 4 at full capacity |
| Mistral | Large > Medium > Small > Ministral. Ministral is intended for edge devices. | Higher is newer (Small 4, Medium 3.5) | “Mistral Small 4” is the small tier, generation 4 |
| TAIDE | Follows the open-source convention of naming by parameter count: a number followed by B for billion. Larger is more capable. | Follows the base model generation (Llama 2 to 3 to 3.1) | “TAIDE-LX-13B” is larger than 7B. The newer release moved to Llama 3.1 at approximately 8B. |
The most useful rule for everyday users: do not use a large model for a simple task
This point matters, because a beginner’s instinct is to assume that the most capable model is the safest choice. The opposite is true. For everyday tasks such as classification, extraction, summarization, simple questions, and format conversion, the smallest and least expensive tier — Haiku, Flash, or mini — is more than sufficient. Reserve the largest and most expensive models for genuinely difficult reasoning, long document analysis, or complex programming.
The intelligent approach is to work in tiers. Send high-volume, low-risk routine work to small models, and escalate to a flagship model only when a task is difficult enough to require it. Using the most expensive model for everything is both slow and wasteful.
In other words, selecting a model involves two decisions. First decide which vendor suits this kind of task (part one), then decide which size within that vendor matches your difficulty and budget (part two). Getting both right is what distinguishes someone who really knows how to use AI.
What to consider when selecting a language model
Before introducing generative AI, a company has to review the question from the legal level through to the technical level. Selecting a language model is not only a matter of parameters and speed. It also requires examining compliance and privacy, ensuring adherence to GDPR and the EU AI Act, assessing whether data can be isolated, whether domain knowledge is available, and whether multilingual and cultural adaptation is adequate. It requires judging the balance between reasoning, tool integration, creativity, and precision, while weighing cost, latency, and long-context capability. Finally, controllable permissions, a complete ecosystem, and service level agreements are also critical. The ten considerations below provide a complete map.
| # | Consideration | Key question | Typical scenario |
|---|---|---|---|
| 1 | Compliance and risk management | Does the model provide compliance statements such as GDPR or the EU AI Act, and the option and assurance that user data will not be used for training? | Healthcare, finance, government tenders |
| 2 | Data privacy and protection | Is prompt data fed back into training data? Is permission isolation supported? | Research confidentiality, customer personal data |
| 3 | Industry domain knowledge | Can it be supplemented with specialist knowledge and specific requirements in areas such as law, medicine, or code? | Contract review, code review |
| 4 | Language and cultural alignment | Does it support less widely spoken languages, such as the Nordic languages? Does it handle local humor? | International marketing copy |
| 5 | Reasoning and tool integration | Does it support agents and different methods of accessing external data? | Automated workflows |
| 6 | Creativity versus precision | Is the writing rich or rigorous and conservative? What is the hallucination rate? | Slogan creation versus regulatory analysis |
| 7 | Cost and latency | What is the price? Is the response measured in milliseconds or seconds? | High-volume text and image description |
| 8 | Context window length | Can it handle 128k, 200k, or one million tokens? | Summarizing an entire manual |
| 9 | Controllability | Can user permissions be managed and usage behavior traced? | Controlling the use of sensitive data |
| 10 | Ecosystem and support | Service level agreement, dedicated support, an extensible marketplace? | Uninterrupted SaaS service |
Timing note: the enforcement powers of the EU AI Act, including requests for information, model access, and recall, take effect from 2 August 2026. The associated compliance requirements may continue to change, so please refer to the latest official announcements.
Matching tasks to models in practice
The following examples use a division of labor between one Primary and one Secondary. The Primary is not necessarily a plain model. It can also be an AI agent you have built in advance, which makes the division of labor fit your task more closely. This is one of the ways the MAIA mode in AnyInsight.ai is used.
| Task | What the Primary needs | What the Secondary contributes |
|---|---|---|
| Research document summary | Support for a long context window | A creativity-oriented model rewrites the summary titles and highlights |
| Marketing copy generation | Rich style with strong emotional tone | A rigorous model checks regulatory compliance and brand tone |
| Legal contract review | Domain fine-tuning and a low hallucination rate | Comparison across models identifies potential contradictions |
| Internal knowledge retrieval | Permission control and protection of private knowledge | An open-source model builds the vector index and handles embedding queries |
The new normal of AI transformation
As open-source and commercial LLMs continue to proliferate, knowing how to choose and how to combine has become an important capability for companies pursuing AI transformation. Through clear evaluation criteria, mixed workflows, and continuous governance, a company should not aim only at optimizing performance or cost at a single point. The aim is to build an efficient, controllable, and secure ecosystem, whether it uses one model or several.
Under conventional thinking, however, a company that wants to deploy and operate one or more LLMs itself, particularly on-premises, usually faces substantial costs in hardware, DevOps staff, model licensing, and security governance, and still cannot keep pace with the rapid progress of cloud-based LLMs. For most companies with limited resources, this is a threshold that makes the project impractical even when it is desirable.
AnyInsight.ai provides a multi-model collaboration platform on a cloud SaaS architecture precisely to address this problem, while allowing companies to choose between one model and several. The next article explains the design principles of the AnyInsight.ai multi-model platform, including how multiple AI choices are applied in conversation and in agent design, and the MAIA (Multi-AI Architecture) mode.
Frequently asked questions
Which AI model is the best?
There is no single best model. Recent third-party evaluations consistently reach the same conclusion: the correct approach is to assign each task to the model best suited to it. Which model is best depends on your task, your budget, your latency requirements, and your data privacy requirements.
What is the difference between Gemini Pro and Gemini Flash?
They are different tiers within the same family. Pro is the more capable and more expensive tier. Flash is optimized for speed and cost. For everyday tasks such as summarization and classification, Flash is usually sufficient.
What do Opus, Sonnet, and Haiku mean in Claude model names?
They indicate size, from largest to smallest. A useful memory aid is poem length: the shorter the poem, the smaller and faster the model. Opus is the most capable tier, Sonnet is the middle tier, and Haiku is the lightest and fastest.
What is a context window?
The context window is how much text a model can hold at one time, measured in tokens. A model with a 128k context window can work with a much shorter document than one with a one-million-token window. Long context matters when you need to summarize an entire manual or analyze a large set of documents at once.
Should I use one model or several?
For an individual working on routine tasks, one model is usually enough. For a company, routing different tasks to different models generally produces better results, provided all of them are managed on a single platform. Without unified governance, using several models produces inconsistent policies and fragmented data protection.
The Series, Start to Finish
This was Part 4 of Productivity.
