CCDV-F Deep Dive 10/25: Model Selection and Trade-offs (Domain 5, 2.7%) | Claude Certified Developer

Model Selection and Trade-offs explained for the CCDV-F exam: choosing a Claude model means picking the cheapest, fastest model that clears your quality bar on your own evaluation set, weighing capability, speed and cost together. Use effort as a lever inside one model, check context and output limits, and treat every model change as a system change: rerun your evals and read the migration guide for breaking changes before you switch. Model Selection and Trade-offs carries 2.7% of the Claude Certified Developer Foundations (CCDV-F) exam weight, in Domain 5: Model Selection and Optimization (16.8%). This exam-prep deep dive is for developers preparing for the Claude Certified Developer Foundations (CCDV-F) exam. It covers the official Model Selection and Trade-offs scope: the capability, speed and cost trade-off, the roles of the Opus, Sonnet and Haiku tiers with example use cases, efficiency-first versus capability-first starting points, a repeatable eval-driven selection process, common traps, routing and multi-model strategies, the effort parameter, adaptive thinking support versus extended thinking, context limits, the model lifecycle, and a checklist of breaking behaviour changes across model releases (sampling parameters, prefill, extended thinking, thinking that cannot be disabled, forced tool choice, and unmodified thinking blocks). Model names, prices, limits and the exact model lists change, so check the current documentation. It is an independent study video, not official Anthropic material, and the practice questions are original. What you will learn: • The capability, speed and cost trade-off, and effort as a fourth criterion • Tier roles: when Opus, Sonnet or Haiku fits a workload • Efficiency-first versus capability-first, and an eval-driven selection process • Routing by difficulty, and the advisor and orchestrator multi-model strategies • Effort and adaptive thinking support across models • Why context window and maximum output can rule a model out • Breaking changes across model releases, as a migration checklist • Model lifecycle: pinned IDs, deprecation, retirement, and a worked example CHAPTERS 00:00 Model Selection and Trade-offs deep dive for the CCDV-F exam: scope and exam weight 00:49 LLM trade-offs: capability vs speed vs cost when choosing a Claude model 01:36 Claude Opus vs Sonnet vs Haiku: tier roles and example use cases 02:29 How to choose a Claude model: efficiency-first vs capability-first 03:26 Model selection process: define the bar, build evals, compare quality, latency and cost 04:15 Model selection mistakes: biggest model by default, benchmarks, one-dimension optimisation 05:08 LLM routing by difficulty and multi-model strategies: advisor and orchestrator 06:00 Claude effort parameter: trading thoroughness for tokens and latency within one model 06:49 Claude adaptive thinking vs extended thinking: which models support which 07:36 Context window and max output limits when choosing a model, and the Models API 08:36 Switching Claude models safely: prompts, effort sweep, tokenizer, re-run evals 09:28 Claude breaking changes across model releases: sampling parameters, prefill, extended thinking 10:19 More Claude breaking changes: thinking always on, forced tool choice, unmodified thinking blocks 11:15 Claude model lifecycle: pinned model IDs, deprecation, retirement, usage audit 12:06 Worked example: choosing a Claude model for tagging, customer chat, and a coding agent 13:00 Practice questions and answers: Model Selection and Trade-offs CCDV-F deep-dive series: video 10 of 25. Each deep dive goes further than the matching short lesson in the CCDV-F full course; watch that lesson first if the topic is new. Previous: CCDV-F Deep Dive 09/25, Cost and Token Management:    • CCDV-F Deep Dive 09/25: Cost and Token Man...   Go deeper on this channel: CCDV-F 10/25: Model Selection and Trade-offs (Domain 5, 2.7%) | Claude Certified Developer:    • CCDV-F 10/25: Model Selection and Trade-of...   Exam format: 53 multiple-choice and multiple-response questions, 120 minutes, pass mark 720 of 1000 (scaled). Weights and format come from third-party summaries of Anthropic's CCDV-F Exam Guide v1.0 (July 2026); confirm them against Anthropic's official guide before you sit the exam. Subscribe to Core Concept Learning for clear, visual explanations of AI and software engineering concepts. #CCDVF #ClaudeCertifiedDeveloper #ModelSelection