The Superpowers of Every Major LLM — And When to Use Each One

We get asked constantly: which AI model should we use? The honest answer is that it depends entirely on what you're building. After shipping multiple AI-native products, here's what we've learned about each model's strengths.

GPT-4o — The Versatile Powerhouse

Best for: Complex reasoning, nuanced instruction-following, conversational AI

GPT-4o remains the most well-rounded model available. It excels at tasks that require understanding subtle context, following multi-step instructions, and generating human-quality text. It's our go-to for products that need sophisticated conversational AI — the kind where users expect the system to "just understand" what they mean.

The multimodal capabilities are genuinely useful in production. Being able to process images, audio, and text through a single model simplifies architecture significantly. For products like Jortty, where users might describe a tech problem verbally or send a screenshot, this flexibility is invaluable.

Claude — The Thoughtful Analyst

Best for: Long-context analysis, careful reasoning, document processing

Claude's 200K context window isn't just a spec sheet number — it fundamentally changes what's possible. We use Claude extensively for products that involve processing long documents, analyzing complex datasets, or maintaining coherent conversations over extended interactions.

What sets Claude apart is the quality of its reasoning. It tends to be more careful, more nuanced, and more willing to express uncertainty than other models. For applications where accuracy matters more than speed — legal tech, healthcare, financial analysis — Claude is often the right choice.

Gemini — The Multimodal Native

Best for: Multimodal tasks, reasoning across text/images/code, Google ecosystem integration

Gemini shines when you need to reason across different types of content simultaneously. Analyzing a chart and explaining the trends. Understanding a codebase from screenshots. Processing video content with natural language queries. These cross-modal tasks are where Gemini consistently outperforms.

Llama & Mistral — The Self-Hosted Champions

Best for: Data privacy, no API costs at scale, custom fine-tuning

Open-source models like Llama 3 and Mistral give you something proprietary APIs can't: complete control over your data and your costs. For products where data privacy is non-negotiable — healthcare, finance, government — self-hosting is often a requirement, not a preference.

The performance gap between open-source and proprietary models has narrowed dramatically. Llama 3 70B is competitive with GPT-4 on many benchmarks, and Mistral's smaller models offer remarkable performance-per-parameter ratios. Combined with frameworks like OpenClaw, self-hosted models are increasingly viable for production workloads.

DeepSeek — The Coding Specialist

Best for: Code generation, technical problem-solving, mathematical reasoning

DeepSeek has emerged as a surprisingly capable model, particularly for coding tasks. Its code generation quality rivals GPT-4o in many scenarios, and its mathematical reasoning capabilities are impressive. We've started using it for internal tooling and code review automation with strong results.

The Real Superpower: Knowing When to Switch

The real competitive advantage isn't picking one model — it's architecting your product so you can leverage the right model for each task. A single product might use GPT-4o for conversation, Claude for document analysis, and Llama for privacy-sensitive local processing.

This is what AI-native development actually means. Not committing to a single vendor, but building systems that are model-agnostic by design — so you can adapt as the landscape evolves.

Building an AI product and not sure which model to use? Let's figure it out together. We've shipped enough AI products to know that the right answer is almost never "just use ChatGPT."