New models come out every few weeks, and every leaderboard crowns a different winner. For a company, the more important question is: which model is best for my task at an acceptable price?
Criteria
- Quality for the specific task: writing, analysis, coding, data extraction.
- Language: how the model writes in the languages of your customers.
- Speed: critical for chat and support.
- Price per token: differences between models can be tens of times.
- Context: how much text the model can process at once.
- Privacy: where data is processed and under what terms.
A rough rule
- Top models (the flagship models from OpenAI, Anthropic and Google) for complex analysis, writing and coding.
- Mid-size and small models for classification, short answers, routing and high volumes.
- DeepSeek and similar for an excellent price to quality ratio on many tasks.
- Open models (Llama, Qwen, Mistral) when data must stay local.
Do not pick just one
The best results come from a multi-model approach: each task goes to the model that solves it best and cheapest. In Intrascope a team gets 120+ models in one place, and admins control who can use which models and how much they spend.
How to test
Take 20 to 30 real tasks from your work and run them through 3 to 4 models. Score accuracy, style and cost. That is more reliable than any leaderboard. Estimate cost per task with the LLM API cost calculator.
If you need help choosing and rolling out, see AI automation or try our free AI chat.
Frequently asked questions
Which AI model is best for non-English languages?
Leading models from the big providers handle most European languages well. Differences show in style and grammar, so test with your own examples.
Why use several models?
Because different models are best for different tasks and prices. Smart routing sends simple tasks to cheaper models and complex ones to stronger models.
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