Session 0· 04· 10 min
LLM Providers Compared
What you'll learn
- ▸Know the major commercial LLM providers and what they are good at
- ▸Understand the difference between closed and open-source models
- ▸See a feature matrix so you can pick for a specific project
The LLM market has ~4 dominant commercial providers and a large open-source ecosystem. In this course you will mainly use the first three — they all expose a similar API surface, and LangChain (Session 6) lets you swap between them with one line.
OpenAI
The incumbent — GPT family
Flagship models
gpt-4ogpt-4o-minigpt-4.1o1
Best for
- · Most mature ecosystem & tooling
- · Best for general-purpose chat + agents
- · Excellent function calling
- · Native image + TTS + STT endpoints
platform.openai.com
Anthropic
Claude — safety-first frontier
Flagship models
claude-sonnet-4-5claude-opus-4-6claude-haiku-4-5
Best for
- · Longest coherent reasoning
- · Strong on code and analysis
- · Very large 200k+ context
- · Lower hallucination rate
console.anthropic.com
Google
Gemini — multimodal long-context
Flagship models
gemini-2.5-progemini-2.0-flash
Best for
- · Massive context (up to 2M tokens)
- · Strong multimodal (video, audio)
- · Competitive pricing for the class
- · Tight integration with Google Cloud
aistudio.google.com
Meta / Open-source
Llama, Mistral, Qwen, DeepSeek
Flagship models
llama-4mistral-largeqwen-2.5deepseek-v3
Best for
- · Free — run on your own hardware
- · Full weight access, fine-tuneable
- · No vendor lock-in
- · Privacy — data never leaves your infra
huggingface.co
Commercial vs open-source — what changes
| Commercial (OpenAI, Anthropic, Google) | Open-source (Llama, Mistral…) | |
|---|---|---|
| Setup effort | API key | GPU + model weights |
| Pay per use | ||
| Best frontier quality | ||
| Data stays on your infra | ||
| Fine-tuneable | ||
| Latency under your control | ||
| Function calling out of the box | ||
| Good for learning / prototyping |
Side-by-side feature matrix (commercial)
| OpenAI | Anthropic | Google Gemini | |
|---|---|---|---|
| General chat quality | excellent | excellent | very good |
| Code generation | excellent | excellent | good |
| Long-context reasoning | good | excellent | excellent |
| Max context window | 400k | 200k | 2M |
| Function / tool calling | |||
| Structured output (JSON schema) | |||
| Vision input | |||
| Image generation | |||
| Audio (TTS + STT) | |||
| Pricing tier (cheapest) | $$ | $$$ | $ |
Why LangChain supports all three
Each provider has its own SDK with slightly different method names. LangChain wraps them behind a unified interface (ChatOpenAI, ChatAnthropic, ChatGoogleGenerativeAI) so the same code runs on any of them. You will see this in Session 6.
Other providers worth knowing
- Mistral — French startup, strong open-weight and API models, competitive Euro-friendly pricing.
- Cohere — enterprise focus, best-in-class embeddings and reranking for RAG.
- xAI (Grok) — Elon Musk's company, real-time X/Twitter integration.
- DeepSeek — Chinese lab, extremely cheap reasoning models.
- Groq / Together AI / Fireworks — inference providers that serve open-source models at blazing speed (not model makers).
What to remember
For this course you only need keys for OpenAI (Sessions 1 & 5), plus Anthropic and Google (Session 6). Once you understand one provider, all the others feel familiar — the vocabulary is identical.