Intermediate~2h
Provider Landscape in LLM CoreInterview Readiness Check
OpenAI vs Anthropic vs Google vs open-source — what each is genuinely best at in 2026, when each loses, pricing trends, the second-source argument, lock-in..
OpenAIAnthropicClaudeGPTGeminiLlamaMistralDeepSeekproviderlock-in
Learning path
What LLMs Actually Are→Provider Landscape ← you are here→Cost & Token EconomicsOpen-source Models & Self-hostingVision, Audio & Multimodal LLMs
Try a sample question
A startup is building a customer-facing chatbot that handles 50,000 conversations per day. Each conversation averages 500 input + 200 output tokens. Budget is tight. Which factor should most influence their model selection?
A.Always choose the largest model available — bigger models are always better
B.Select based on developer familiarity — the team's preferred language SDK should determine the provider
C.Choose whichever model has the longest context window to future-proof the system
D.Cost per million tokens combined with quality on the actual task — benchmark cheaper models (e.g., GPT-4o-mini, Gemini Flash, Claude Haiku) against the full model on real examples before defaulting to the expensive tier
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of provider landscape fundamentals
- Trade-offs and design decisions in real system scenarios
- OpenAI and Anthropic — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Provider Landscape check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Provider Landscape check test?
- The check covers openai vs anthropic vs google vs open-source — what each is genuinely best at in 2026, when each loses, pricing trends, the second-source argument, lock-in.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Provider Landscape?
- Key concepts include: OpenAI, Anthropic, Claude, GPT, Gemini, Llama, Mistral, DeepSeek, provider, lock-in. These frequently appear in llm core interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Provider Landscape?
- Most engineers spend around 2 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Provider Landscape?
- Provider Landscape builds on What LLMs Actually Are. Complete those first for the best results.
- Is OpenAI, Anthropic, Claude covered?
- Yes. The Provider Landscape module covers OpenAI, Anthropic, Claude among other key concepts. The check includes questions that reflect real llm core interview patterns.