Model Selection Guide

Select the optimal model for your agent based on your goals and use case.

Choosing the right model is essential to building effective agents. This guide helps you evaluate trade-offs, pick the right model for your use case, and iterate quickly.

Key considerations

• Accuracy and output quality: Advanced logic, mathematical problem-solving, and multi-step analysis may require high-capability models.
• Domain expertise: Performance varies by domain (for example, creative writing, code, scientific analysis). Review model benchmarks or test with your own examples.
• Context window: Long documents, extensive conversations, or large codebases require models with longer context windows.
• Embeddings: For semantic search or similarity, consider embedding models. These aren’t for text generation.
• Latency: Real-time apps may need low-latency responses. Smaller models (or “Mini,” “Nano,” and “Flash” variants) typically respond faster than larger models.

Models by task / use case at a glance

Task / use case	Example models	Key strengths	Considerations
General-purpose conversation	Claude 4 Sonnet, GPT-4.1, Gemini Pro	Balanced, reliable, creative	May not handle edge cases as well
Complex reasoning and research	Claude 4 Opus, O3, Gemini 2.5 Pro	Highest accuracy, multi-step analysis	Higher cost, quality critical
Creative writing and content	Claude 4 Opus, GPT-4.1, Gemini 2.5 Pro	High-quality output, creativity, style control	High cost for premium content
Document analysis and summarization	Claude 4 Opus, Gemini 2.5 Pro, Llama 3.3	Handles long inputs, comprehension	Higher cost, slower
Real-time apps	Claude 3.5 Haiku, GPT-4o Mini, Gemini 1.5 Flash 8B	Low latency, high throughput	Less nuanced, shorter context
Semantic search and embeddings	OpenAI Embedding 3, Nomic AI, Hugging Face	Vector search, similarity, retrieval	Not for text generation
Custom model training & experimentation	Llama 4 Scout, Llama 3.3, DeepSeek, Mistral	Open source, customizable	Requires setup, variable performance

Hypermode provides access to the most popular open source and commercial models through Hypermode Model Router documentation. We’re constantly evaluating model usage and adding new models to our catalog based on demand.

Get started

You can change models at any time in your agent settings. Start with a general-purpose model, then iterate and optimize as you learn more about your agent’s needs.

1. Create an agent with GPT-4.1 (default).
2. Define clear instructions and connections for the agent’s role.
3. Test with real examples from your workflow.
4. Refine and iterate based on results.
5. Evaluate alternatives once you understand patterns and outcomes.

Value first, optimize second. Clarify the task requirements before tuning for specialized capabilities or cost.

Comparison of select large language models

Model	Best For	Considerations	Context Window+	Speed	Cost++
Claude 4 Opus	Complex reasoning, long docs	Higher cost, slower than lighter models	Very long (200K+)	Moderate	$$$$
Claude 4 Sonnet	General-purpose, balanced workloads	Less capable than Opus for edge cases	Long (100K+)	Fast	$$$
GPT-4.1	Most tasks, nuanced output	Higher cost, moderate speed	Long (128K)	Moderate	$$$
GPT-4.1 Mini	High-volume, cost-sensitive	Less nuanced, shorter context	Medium (32K-64K)	Very Fast	$$
GPT o3	General chat, broad compatibility	May lack latest features/capabilities	Medium (32K-64K)	Fast	$$
Gemini 2.5 Pro	Up-to-date info	Limited access, higher cost	Long (128K+)	Moderate	$$$
Gemini 2.5 Flash	Real-time, rapid responses	Shorter context, less nuanced	Medium (32K-64K)	Very Fast	$$
Llama 4 Scout	Privacy, customization, open source	Variable performance	Medium-Long (varies)	Fast	$

+ Context window sizes are approximate and may vary by deployment/version.

^{++ Relative cost per 1K tokens ($ = lowest, $$$$ = highest)}