How to Choose the Right LLM for Your Task

Not all language models are created equal. Each model has different strengths, and picking the right one for your task can make a dramatic difference in the quality of your results. Whether you are drafting an email, writing code, translating between languages, or just asking a quick question, the model you choose matters. VoxyAI supports both Ollama models and Apple Intelligence, giving you the flexibility to match the right tool to the job.

This guide walks you through the most popular models available through Ollama and Apple Intelligence, explains what each one excels at, and helps you decide which to use based on what you are actually trying to accomplish.

Why the Model You Choose Matters

Language models are trained on different datasets, with different architectures, and for different purposes. A model that is excellent at generating Python code might produce mediocre translations. A model that writes beautiful prose might struggle with structured data extraction. And a model that responds in milliseconds might sacrifice depth for speed.

The key insight is this: you can ask the same question to two different models and get very different results. One might give you a concise, accurate answer while the other gives you a vague or incorrect one, simply because one model was designed for that type of task and the other was not. Choosing the right model is not about finding the "best" one. It is about finding the best one for what you need right now.

Apple Intelligence: Speed and Privacy Built In

If you have a Mac with Apple Silicon, you already have a language model available to you: Apple Intelligence. It is deeply integrated into macOS and optimized specifically for Apple hardware, which gives it a significant speed advantage over most local alternatives.

Apple Intelligence is exceptionally fast. Because it runs as a native system service optimized for the Neural Engine in Apple Silicon, responses come back almost instantly. For quick tasks like formatting dictation, fixing grammar, or rephrasing a sentence, this speed is hard to beat. You can speak naturally and see your words formatted in real time with virtually no delay.

The trade-off is context size. Apple Intelligence works with a relatively small context window, which means it handles short to medium-length inputs very well but can struggle with longer documents or complex multi-step prompts. If you are formatting a few paragraphs of dictation or asking a straightforward question, Apple Intelligence is an excellent choice. If you are trying to summarize a ten-page report or maintain a long back-and-forth conversation, you will want to reach for an Ollama model with a larger context window instead.

Best uses for Apple Intelligence:

• Quick dictation formatting and punctuation
• Grammar correction and sentence rephrasing
• Short question-and-answer interactions
• Tasks where speed is more important than depth

Ollama Models: Power and Flexibility

Ollama gives you access to a wide range of open-source models, each with different specializations. Unlike Apple Intelligence, which is a single model with a fixed set of capabilities, Ollama lets you install multiple models and switch between them depending on the task. This flexibility is one of its greatest strengths.

Below is a breakdown of the most popular Ollama models and what each one does best.

Llama: The Versatile All-Rounder

Meta's Llama family of models is the most widely used open-source LLM for good reason. Llama models are trained on a massive multilingual dataset, which makes them particularly strong at understanding and generating text in many languages. If you frequently work in multiple languages or need to translate between them, Llama is an excellent choice.

Llama 3.2 comes in several sizes. The 3-billion-parameter version is small enough to run comfortably on Macs with just 8 GB of RAM, while larger versions are available for machines with more memory. Even the smaller variant handles everyday tasks like drafting emails, answering questions, and formatting dictation with impressive quality.

Why Llama excels at translation and multilingual tasks:

• Trained on text spanning dozens of languages, so it understands grammar and idioms across language families
• Handles translation between common language pairs with strong accuracy
• Can write, summarize, and rephrase content in languages beyond English
• Excellent for multilingual dictation formatting, recognizing context clues in non-English text

ollama pull llama3.2

CodeLlama: Built for Developers

CodeLlama is a specialized variant of Llama that has been fine-tuned specifically on code. It understands programming languages, frameworks, and software patterns in a way that general-purpose models simply cannot match. If you write code regularly, CodeLlama should be one of your go-to models.

What sets CodeLlama apart is its ability to understand code context. It does not just generate syntactically correct code. It understands what your code is trying to do and can suggest completions, find bugs, explain unfamiliar code, and even refactor existing functions. It supports Python, JavaScript, TypeScript, Java, C++, Go, Rust, and many other popular languages.

Why CodeLlama excels at programming tasks:

• Fine-tuned on billions of lines of source code across many programming languages
• Understands code structure, design patterns, and language-specific idioms
• Can generate functions, classes, and entire modules from natural language descriptions
• Strong at debugging: paste an error message and it can often identify the root cause
• Handles code explanation, making it useful for learning new languages or understanding unfamiliar codebases

ollama pull codellama

Mistral: The Balanced Choice

Mistral occupies a sweet spot between speed and quality. At around 4 GB, it is small enough to run responsively on most Macs but large enough to produce noticeably better output than the smallest models. Mistral is a strong general-purpose model that handles writing, summarization, question answering, and analysis with consistent quality.

If you are not sure which model to start with and want a single reliable option, Mistral is often the best default choice. It rarely produces the best output for any single specialized task, but it rarely produces poor output for anything either. It is the dependable all-purpose tool in your toolkit.

Why Mistral excels as a general-purpose model:

• Strong performance across writing, analysis, summarization, and Q&A
• Good balance of speed and output quality at its size
• Reliable instruction following with fewer hallucinations than many competitors
• Works well for everyday dictation formatting and professional communication

ollama pull mistral

Gemma: Strong Reasoning and Creative Writing

Google's Gemma models punch above their weight when it comes to reasoning and creative tasks. If you need a model that can follow complex instructions, think through multi-step problems, or produce creative and engaging writing, Gemma is worth trying. It also performs well at analysis tasks like breaking down arguments, comparing options, or extracting insights from text.

Why Gemma excels at reasoning and creative work:

• Strong instruction following, especially for multi-step tasks
• Produces creative, well-structured writing for stories, marketing copy, and content
• Good at analytical tasks like comparing pros and cons or summarizing arguments
• Handles nuanced prompts better than many models in its size class

ollama pull gemma2

Phi: Maximum Speed on Minimal Hardware

Microsoft's Phi models are designed to deliver surprisingly strong results from a very small package. At roughly 2 GB, Phi runs quickly even on Macs with just 8 GB of RAM. If speed is your top priority or your hardware is constrained, Phi is an excellent option. It punches above its weight for reasoning tasks and handles straightforward questions well.

Why Phi excels on constrained hardware:

• One of the smallest models available while still being genuinely useful
• Very fast responses, even on entry-level Apple Silicon Macs
• Good at reasoning and logic tasks relative to its size
• Leaves plenty of RAM available for other applications

ollama pull phi3

Matching the Model to Your Task

Here is a practical guide to choosing the right model based on what you are trying to do:

Task	Recommended Model	Why
Quick dictation formatting	Apple Intelligence	Fastest response time, ideal for real-time formatting
Writing code or debugging	codellama	Purpose-built for programming with deep code understanding
Translating or multilingual work	llama3.2	Trained on extensive multilingual data across dozens of languages
General writing and emails	mistral	Best balance of quality and speed for everyday communication
Creative writing and brainstorming	gemma2	Strong creative output and nuanced instruction following
Analysis and reasoning	gemma2	Excellent at multi-step reasoning and structured analysis
Quick questions on limited hardware	phi3	Smallest footprint with fast responses on 8 GB Macs
Long document summarization	mistral / llama3.2	Larger context windows handle more text than Apple Intelligence

A Quick Note on RAM and Model Size

Larger models generally produce higher-quality output but require more RAM. The relationship is straightforward: the more parameters a model has, the more memory it needs and the more nuanced its responses tend to be. If you have 8 GB of RAM, stick with smaller models like Llama 3.2 (3B) or Phi3. With 16 GB, you can comfortably run 7B models like Mistral. If you have 32 GB or more, you can explore larger variants that deliver noticeably better results for complex tasks.

That said, do not assume bigger is always better for your specific use case. A smaller model that is specialized for your task will often outperform a larger general-purpose model. CodeLlama at 7B parameters will write better code than a generic 13B model, and Apple Intelligence will format your dictation faster than any Ollama model regardless of size.

Experiment and Compare

One of the best things about having access to multiple models is that you can try them side by side. Ask the same question to two or three different models and compare the results. You might be surprised at how different the outputs can be. One model might give you a technically correct but dry answer, while another provides a more natural and engaging response to the same prompt.

VoxyAI makes this easy. You can switch between Apple Intelligence and any of your installed Ollama models right from the settings. Try formatting your dictation with Apple Intelligence for speed, then switch to Mistral when you need more thoughtful formatting for a professional email, or to CodeLlama when you are dictating code comments and function descriptions.

There is no single perfect model for every situation. The best approach is to keep a few models installed and reach for the one that fits the task at hand. With VoxyAI, switching is instant, and experimenting costs nothing.