Learn to evaluate and choose the right LLM for your task — from GPT to Claude to open-source models.
The LLM ecosystem has evolved into a competitive landscape with several major players. Each provider brings different strengths, pricing models, and design philosophies. Understanding the landscape is the first step to making informed choices.
GPT-4o — fast, multimodal flagship. o1 / o3 — reasoning models with chain-of-thought. Strong at coding, general tasks, and broad tool ecosystem.
Claude 4.5 Sonnet — balanced speed and intelligence. Claude Opus 4 — top-tier reasoning and analysis. Known for safety, long context (200K), and instruction following.
Gemini 2.5 Pro — massive 1M token context window. Gemini 2.5 Flash — fast and cost-effective. Native multimodal support for text, images, audio, and video.
Llama 4 — leading open-source family. Free to use, can run locally or on your own infrastructure. Strong community, fine-tuning ecosystem, and full data control.
Picking an LLM is like choosing a vehicle. A sports car (GPT-4o) is fast and flashy. A luxury sedan (Claude Opus 4) is refined and comfortable for long trips. An SUV (Gemini 2.5 Pro) handles massive cargo. And building your own kit car (Llama 4) gives you total control — but you need to do the assembly.
Choosing the right model isn't about finding the "best" one — it's about finding the best fit for your specific requirements. Here are the key factors to evaluate:
| Factor | Description | Example |
|---|---|---|
| Quality / Intelligence | How well the model handles complex reasoning, nuance, and accuracy | Claude Opus 4 and GPT-4o lead on hard reasoning tasks |
| Speed / Latency | Time to first token and overall generation speed | Gemini Flash and GPT-4o-mini respond in under 500ms |
| Cost (per token) | Input and output token pricing — adds up fast at scale | GPT-4o-mini is ~50x cheaper than GPT-4o per token |
| Context Window | Maximum tokens the model can process in a single request | Gemini 2.5 Pro: 1M tokens, Claude: 200K, GPT-4o: 128K |
| Multimodal Support | Ability to process images, audio, video, and files | Gemini handles video natively; GPT-4o and Claude handle images |
| Safety / Alignment | How well the model avoids harmful outputs and follows guidelines | Claude is known for strong alignment; important for regulated industries |
| Open Source vs Proprietary | Whether you can inspect, modify, and self-host the model | Llama 4 is open-source; GPT-4o and Claude are proprietary APIs |
Most teams only have 2-3 factors that truly matter. A startup building a chatbot cares most about cost and speed. A legal firm processing contracts cares about accuracy and context window. Identify your non-negotiables first.
The AI community uses standardized benchmarks to compare models. While imperfect, they provide a useful starting point for understanding model capabilities.
| Benchmark | What It Measures | Why It Matters |
|---|---|---|
| MMLU | Massive Multitask Language Understanding — 57 academic subjects | Broad knowledge across domains (history, science, law, etc.) |
| HumanEval | Code generation — writing Python functions from docstrings | Critical for coding assistants and developer tools |
| GPQA | Graduate-level science questions (physics, chemistry, biology) | Tests deep expert-level reasoning |
| MATH | Competition-level mathematics problems | Tests formal reasoning and step-by-step problem solving |
| Arena ELO | Human preference rankings from blind A/B comparisons (Chatbot Arena) | The closest proxy to "what do real users prefer?" |
Benchmarks measure general tasks, but real-world performance often differs from benchmark scores. A model that scores 92% on MMLU might still struggle with your specific domain. Models can also be optimized for benchmarks in ways that don't generalize. Always treat benchmarks as a starting point, not a final answer.
The most reliable evaluation is testing 2-3 candidate models on your actual data and tasks. Create a set of 20-50 representative examples from your real workload, run them through each model, and compare the outputs. This "vibe check" is worth more than any benchmark leaderboard.
One of the biggest decisions in model selection is whether to use a proprietary API (OpenAI, Anthropic) or an open-source model (Llama, Mistral) that you can host yourself. Each path has distinct trade-offs.
Proprietary: Pay per token, predictable pricing, no infra overhead. Open-source: Free model weights, but you pay for GPU compute and ops.
Proprietary: Data goes to a third-party API. Open-source: Full control — data never leaves your infrastructure. Critical for healthcare, finance, and government.
Proprietary: Limited to fine-tuning APIs and prompt engineering. Open-source: Full fine-tuning, LoRA adapters, quantization, and architecture modifications.
Proprietary: Generally highest raw quality on hard tasks. Open-source: Catching up fast — often sufficient for focused, narrow use cases.
Here is how to get started with a local open-source model using Ollama:
# Install Ollama curl -fsSL https://ollama.com/install.sh | sh # Pull and run Llama 4 ollama pull llama4 ollama run llama4 "Explain quantum computing in 2 sentences"
And you can integrate it into Python applications just as easily:
# Install: pip install ollama import ollama # Chat with a local model — no API key needed response = ollama.chat( model="llama4", messages=[ {"role": "user", "content": "Explain quantum computing in 2 sentences"} ] ) print(response["message"]["content"]) # List available local models models = ollama.list() for m in models["models"]: print(f"{m['name']} — {m['size'] // 1_000_000_000}GB")
Using a proprietary API is like renting an apartment — easy to start, someone else handles maintenance, but you follow their rules. Running an open-source model is like owning a house — more upfront work, but you can renovate however you want, and nobody can raise your rent.
Use this step-by-step framework to systematically choose the right model for any project. Don't skip straight to "which model is best" — start with your requirements.
Here are common use cases mapped to recommended models:
| Use Case | Recommended Models | Why |
|---|---|---|
| Code generation | Claude Sonnet 4.5 / GPT-4o | Strong coding benchmarks, good at following complex specs |
| Creative writing | Claude Opus 4 / GPT-4o | Nuanced language, strong stylistic control, deep reasoning |
| Data extraction | GPT-4o-mini / Claude Haiku | Fast, cheap, and reliable for structured output at high volume |
| Long document analysis | Gemini 2.5 Pro (1M) / Claude (200K) | Massive context windows handle entire books or codebases |
| On-premise / privacy | Llama 4 / Mistral | Open-source weights, full data control, no API dependency |
| Cost-sensitive | GPT-4o-mini / Claude Haiku / open-source | Smallest models that meet quality bar — orders of magnitude cheaper |
Keep these principles in mind whenever you're evaluating or choosing a model. They'll save you from common pitfalls.
The right model depends on your specific task, budget, and constraints. A $20/month model can outperform a $200/month model for the right use case.
Always benchmark 2-3 models on YOUR actual data before making a decision. What works for others may not work for you.
Smaller, cheaper models often outperform larger ones for narrow, well-defined tasks. Don't default to the most expensive option.
Today's best model may be surpassed next month. Build your system so you can swap models easily — avoid tight coupling to any single provider.
1. Which factor matters MOST when choosing between GPT-4o and Claude Sonnet for a code review tool?
2. When would you choose an open-source model over a proprietary one?
3. Why shouldn't you rely solely on benchmarks to choose a model?
Here's what you've learned:
The LLM landscape spans four major families — OpenAI, Anthropic, Google, and Meta's open-source models. Choosing between them depends on your specific constraints: quality, speed, cost, context window, privacy, and multimodal needs. Benchmarks are a useful starting point but no substitute for testing on your own data. Open-source models give you full control at the cost of operational complexity. The most important principle: there is no universally "best" model — only the best model for your task.
Next up → Topic 3: Prompt Structure Basics
You'll learn about roles, system prompts, instruction design, and how to structure prompts that consistently get great results.