3.2 | Which LLM fits the task? – Deliberate instead of random choices
What you already know
What you will learn in this module
1. Why Model Selection is Important
Choosing the right language model (LLM) is crucial for the success of your AI-powered tasks. Each model, from GPT-4o to Claude 3.7 or Gemini 2.5 pro, has specific strengths, weaknesses, costs, and specializations. An unsuitable model can lead to suboptimal results, increased time investment, or unnecessary costs.
As a Navigator, you can choose from a curated selection of leading models on the xpandAI platform. The ability to identify and use the optimal model for each specific task is a core competency in working with AI and significantly increases your effectiveness.
2. The LLM Landscape: An Overview (as of ~early 2025)
The leading AI companies and open-source communities offer a wide range of language models. Here is an overview of some of the most important players and their current model series:
| OpenAI | GPT-4o (advanced, multimodal), GPT-4 Turbo (powerful, text-focused), GPT-o1/o3 (newer, optimized for reasoning), GPT-3.5 Turbo (fast, cost-effective) |
| Anthropic | Claude 3.7 Sonnet (very powerful, top-tier coding), Claude 3 Opus (previous top model), Claude 3 Haiku (very fast, efficient) |
| Gemini 2.0 Pro/Flash (latest generation, multimodal), Gemini 2.5 Pro (huge context window up to 2M tokens, multimodal) | |
| Meta | Llama 3.1 / 3.2 / 3.3 (leading open source, various sizes 8B-405B+, multimodal in latest versions, 128k context) |
| Mistral AI | Mistral Large 2 (powerful, multilingual), Codestral (specialized in code), Mixtral models (MoE, efficient), Mistral Small 3 (fast) |
| Others / Specialists | DeepSeek R1/V3 (strong reasoning, code, open source), Qwen 2.5 (Alibaba, powerful, open source), Cohere Command R+ (enterprise focus) |
These models differ significantly. Below, we will look at the most important distinguishing features for selection.
Note: Development is extremely fast. New models (e.g., GPT-5, Claude 4, Gemini 3.0 pro) could be available or announced shortly after this status.
3. Key Differentiating Features of the Models
Technical & Functional Differentiation
Context Length (Context Window)
The maximum amount of information (text, code, image data, etc., measured in tokens) that the model can process at once. Ranges from approx. 8,000 tokens to 2,000,000 tokens (Gemini 2.5 Pro).
Relevant for: Analyzing very long documents/books, understanding complex codebases, conducting long conversations, extensive summaries.
Current Knowledge & Web Access
The point in time up to which the model was trained (Knowledge Cutoff) and whether it can access current information from the internet.
Relevant for: Research on current events, market analysis, using the latest APIs/frameworks.
Multimodal Capabilities
The ability to understand and process different types of inputs (text, image, audio, video, code) and to generate various output formats.
Relevant for: Image analysis & creation, audio transcription & generation, video analysis, combined text-image tasks.
Specializations & Performance Profile
Special strengths in areas such as logical reasoning, mathematics, code generation/analysis, creative writing, dialogue skills, or specific languages.
Relevant for: Targeted tasks that require high performance in a specific area (e.g., software development, scientific analysis, marketing texts).
Speed & Cost
Response speed (latency) and cost per processed unit of information (token). Faster/cheaper models (e.g., Haiku, Flash, Llama 8B) vs. more powerful/expensive models (e.g., GPT-4o, Claude 3.7, Gemini Pro).
Relevant for: Real-time applications, budget optimization, scaling of applications.
Open Source vs. Proprietary
Is the model open source (e.g., Llama, Mistral, Qwen, DeepSeek) and can it potentially be self-hosted/customized, or is it a closed system from a provider (e.g., OpenAI, Anthropic, Google)?
Relevant for: Data privacy requirements, customizability, independence, cost control.
4. Comparison Table of Important LLMs (as of ~early 2025)
| Model(-Family) | Strengths | Weaknesses | Best Use Cases | Context Window (approx.) |
|---|---|---|---|---|
| OpenAI GPT (GPT-4o/o1/o3, Turbo) | Very strong reasoning (o1/o3), high all-around capabilities (GPT-4o), good multimodality (image, audio), high code quality, broad API support. | Can be expensive, proprietary, data privacy concerns with sensitive data, sometimes slower response times for top models. | Complex tasks, creative writing, demanding programming, multimodal applications, research. | 128k Tokens (GPT-4o/Turbo) |
| Anthropic Claude (3.5/3.7 Sonnet, Opus, Haiku) | Excellent code generation & analysis (3.5 Sonnet), strong reasoning (3.7 Sonnet), good text processing & dialogue management, focus on security/ethics, artifact usage. | No image generation (only analysis), top models (Opus, 3.7) can be slower/more expensive, proprietary. | Professional software development, document analysis, ethically sensitive tasks, long/complex text content, customer service. | 200k Tokens |
| Google Gemini (2.0 Pro/Flash, 5.5 Pro) | Huge context window (up to 2M tokens), excellent multimodality (image, audio, video), good integration into Google ecosystem, strong factual basis, Flash versions are fast. | Can sometimes be less „creative,“ proprietary, top models/contexts can become expensive. | Analysis of very large amounts of data/videos, multimodal tasks, research with web connectivity, real-time translation/conversations. | 1M – 2M Tokens (Pro), 1M (Flash) |
| Meta Llama (3.1, 3.2, 3.3 – various sizes) | Leading in the open source space, strong performance (esp. 70B+ models), good coding capabilities, high customizability, growing multimodality (3.3), good community support. | May require own infrastructure/hosting, smaller models less powerful, possibly fewer „out-of-the-box“ security features. | Research, development of custom AI applications, on-premise solutions, tasks with a focus on data privacy, good price/performance balance (with self-hosting). | 128k Tokens (newer versions) |
| Mistral AI (Large 2, Codestral, Mixtral, Small 3) | Strong performance (Large 2), excellent code specialization (Codestral), efficient MoE models (Mixtral), open-source options, good performance even with smaller models. | Context window smaller than Gemini/Claude (often 32k-128k), ecosystem still developing compared to OpenAI/Google. | Code generation/optimization (Codestral), efficient text tasks (Mixtral), multilingual applications (Large 2). | 32k – 128k Tokens |
| DeepSeek (R1, V3, Coder) | Excellent reasoning and mathematics (R1), strong coding capabilities (Coder, R1), very good performance for open source models, efficient architecture (MoE). | Focus on specific strengths (reasoning/code), possibly less of an all-rounder than GPT/Claude, community/support still developing. | Scientific research, complex problem-solving, demanding code generation, logic-based tasks. | ~128k Tokens |
5. How Do I Choose the Right Model? (as of ~early 2025)
Decision Tree for Model Selection
Recommendation: Gemini 2.5 Pro
Justification: Largest available context window (1-2 million tokens), strong multimodality.
Top Recommendations: Claude 3.7 Sonnet (very powerful & fast), GPT-4o / o1 (very high quality)
Specialists/Open Source: Mistral Codestral, DeepSeek Coder/R1, Llama 3.x (70B+)
Justification: Excellent performance on coding benchmarks, understanding of complex logic.
Recommendation: GPT-o1 / o3, Claude 3.7 Sonnet, DeepSeek R1
Alternative: GPT-4o, Gemini 2.5 Pro
Justification: Optimized for logical reasoning and complex problem-solving.
Recommendation: Gemini 2.5 Pro (Video!), GPT-4o (Image/Audio strong)
Alternative (Image analysis): Claude 3.7 Sonnet, Llama 3.3
Justification: Comprehensive processing of various media types.
Recommendation: Claude 3 Haiku, Gemini 2.0 Flash, GPT-3.5 Turbo, Mistral Small 3, Llama 3.x (8B)
Justification: Good balance of speed and cost, sufficient for standard tasks.
Recommendation: Llama 3.x (depending on size), Mistral (Mixtral, Codestral), Qwen 2.5, DeepSeek
Justification: Open source, allows for local installation and fine-tuning.
Practical Selection Criteria
- Task Complexity & Specialization: Does the task require deep reasoning (GPT-o1, Claude 3.7), excellent code (Claude 3.5, Codestral), or broad all-around capabilities (GPT-4o)?
- Data Volume/Context: How much information does the model need to process simultaneously? (Gemini Pro for extremely large amounts, Claude/Llama for large, GPT/Mistral for moderate).
- Speed vs. Quality vs. Cost: Fast responses (Haiku, Flash)? Best quality (GPT-o1, Claude 3.7)? Lowest price (smaller models, open source)?
- Media Types: Text only? Or also images, audio, video? (Gemini, GPT-4o are leading).
- Data Privacy/Control: Are proprietary cloud models acceptable, or is an open-source/on-premise solution preferred (Llama, Mistral)?
- Knowledge Freshness: Is access to current web information needed? (Many top models now offer this directly or via plugins).
6. Practice: Model Selection on the xpandAI Platform
The xpandAI platform allows you to seamlessly switch between various integrated language models. This allows you to flexibly choose the most suitable model for your respective task:
- Open the xpandAI platform and select the desired service (e.g., Chat, Content Creation).
- Look for the model selection option (often a dropdown menu, e.g., under „Settings“ or directly in the interface).
- Choose from the available models (e.g., divided into categories like „Fast & Efficient,“ „Powerful,“ „Specialized“). Availability depends on your plan (e.g., Assist vs. Assist Plus).
- Formulate your prompt and observe the results of the chosen model.
Exercise: Model Comparison for a Task
Choose a specific task from your daily work (e.g., drafting a blog post, writing code for a function, composing an email, extracting data from a PDF) and test it with two different models on the Xpand platform:
- Formulate a clear prompt for your task.
- First, run it with a „fast/efficient“ model (e.g., Claude 3 Haiku, Gemini 2.0 Flash, GPT-3.5 Turbo). Note the result and the perceived speed.
- Then, run the same prompt with a „more powerful/specialized“ model (e.g., GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Pro – depending on the task).
- Compare the results: What are the differences in quality, level of detail, creativity, correctness (code)? Is the quality difference worth the potentially higher effort/cost? Was the response time noticeably different?
7. xpand Tip: Cost-Efficiency and Model Selection
Our tip for practical use:
Use a model cascade for optimal results and cost-efficiency. Start with a faster, cheaper model (e.g., Claude 3 Haiku, Gemini 1.5 Flash) for the first draft, simple research, or structuring thoughts.
Only then switch to a more powerful, specialized model (e.g., GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Pro) when it comes to the final draft, complex analyses, critical code sections, or tasks where the highest quality is required.
Example Workflow: Use Gemini 2.0 Flash for a quick summary of a long document, then Claude 3.7 Sonnet to extract and improve specific code examples from it, and finally GPT-4o for the creative formulation of a marketing text based on the results.
8. Summary and Outlook
The selection of the right LLM is a dynamic process, not static knowledge. By experimenting with different models for your specific use cases, you develop a feel for which model delivers the best results and when.
The xpandAI platform offers you the flexibility to easily test and use various top models without having to register with each provider individually. Use this opportunity to deepen your AI competence and maximize your productivity.
Important: The LLM landscape is evolving rapidly. Models that are leaders today may be outdated tomorrow. New breakthroughs in context length, reasoning, multimodality, or efficiency are constantly expected. Stay curious, follow developments (e.g., via LLM leaderboards), and be ready to test new models as they become available.
Your Takeaway (as of ~early 2025)
- Leading LLMs (GPT-4o/o1, Claude 3.7, Gemini 2.5, Llama 3.x, Mistral Large/Codestral, DeepSeek R1) have distinct strengths.
- Key criteria are: task type (text, code, analysis, multimedia), complexity, context length, speed, cost, data privacy (proprietary vs. open source).
- A conscious model selection increases quality, efficiency, and reduces costs.
- Use a cascade: Faster/cheaper models for drafts/standard tasks, more powerful/specialized models for critical/complex parts.
- Stay updated: Development is rapid, regular updates and tests are important.