Beginner Course
Model Selection Beginner
49 lessons across 7 chapters. Every lesson is standalone — start anywhere.
49 lessons 7 chapters
1 Why Model Selection Matters 7 lessons
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No single best model for all tasks The model that wins on benchmarks often fails in production because the benchmark doesn't match your domain, data, latency requirements, or regulatory constraints.
2 Cost, quality, and latency triangle Every production model decision is a forced tradeoff between three constraints that pull against each other: you cannot optimize all three simultaneously.
3 Feature availability differences The model you want may not support the inference framework, deployment region, or cost model your business needs.
4 Vendor lock-in considerations Choosing a vendor's proprietary model now can cost you 2-3x more and 6+ months of rework later when you need to switch.
5 The model landscape: 2026 The model you choose is determined by your constraints: cost, latency, regulation, and data access: not by which model is "best."
6 Selection as an Ongoing Process Your first model choice is never your last: model selection is a continuous cycle driven by data drift, business changes, and new competitive models, not a one-time decision.
7 The cost of wrong model choice Choosing the wrong model early locks you into months of wasted compute, compliance rework, and architectural debt that no amount of fine-tuning will fix.
2 The Model Landscape 2026 7 lessons
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OpenAI: GPT-4.1, GPT-4.1-mini, o1, o3 Model selection isn't about picking the most powerful option: it's about matching inference cost, latency budget, and reasoning depth to your specific problem.
2 Anthropic: Claude Opus 4.6, Sonnet 4.6, Haiku 4.5 Claude models differ by reasoning depth and speed, not just cost: choose based on whether your task needs extended thinking or real-time response.
3 Google: Gemini 2.5 Pro, Flash, Flash-Lite Gemini's three-tier lineup trades cost and latency against reasoning depth: choose based on whether you need thinking or throughput.
4 Meta: Llama 4 Scout, Maverick, Llama 3.3 70B Llama's open-weight models trade proprietary model moats for deployment flexibility and cost predictability: a fundamentally different business model that changes where you can run inference and who controls your data.
5 Mistral: Mistral Large, Small, open source Mistral offers a middle ground between proprietary models and pure open-source: you must choose based on deployment constraints (cloud vs. on-prem), cost sensitivity, and latency requirements, not just capability.
6 DeepSeek: R1 reasoning, V3 efficiency DeepSeek R1 excels at complex reasoning tasks but costs less; V3 prioritizes speed: choose based on latency tolerance, not just capability.
7 Specialized models: code, vision, audio, embedding Different domains require fundamentally different model architectures: picking the wrong one wastes months of engineering and budget.
3 Evaluation Dimensions 7 lessons
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Quality: benchmark scores and real tests A model that scores 95% on a benchmark can fail catastrophically in production because benchmarks measure the wrong thing.
2 Cost: per million tokens input and output Token pricing directly determines whether your AI system is economically viable: and input/output asymmetry means your cost model breaks if you're not careful.
3 Latency: time to first token Time to first token (TTFT) determines whether your AI product feels interactive or broken: and it's determined before you write a single line of code.
4 Context window size Context window size determines what information your model can see at once: pick wrong and you either burn money or miss critical data.
5 Feature set: tools, vision, structured output Model capability selection is not about picking the smartest AI: it's about matching model features to your domain's data format, compliance constraints, and operational reality.
6 Rate limits and availability The model you choose is only useful if you can call it at the scale and frequency your application demands.
7 Data Privacy and Compliance Your model choice is legally locked before you write any code: compliance requirements eliminate 70% of vendor options before technical evaluation begins.
4 Task-Model Matching 7 lessons
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Coding: GPT-4.1, Claude Sonnet, Gemini 2.5 Pro The model you pick determines your cost, latency, reasoning quality, and vendor lock-in risk: choose based on your actual workload, not hype.
2 Reasoning and math: o3, DeepSeek R1, QwQ Reasoning models solve math, code, and logic problems that language models fail on, but they're slower and more expensive: use them only when you actually need step-by-step reasoning.
3 Creative writing: Claude Opus, GPT-4.1 Claude Opus excels at long-form narrative consistency; GPT-4.1 excels at stylistic variety: choose based on whether your application demands coherence or creative range.
4 Document analysis: long context models Long context models let you process entire contracts, medical records, or regulatory filings at once, but the cost and latency trade-offs depend heavily on your document type and compliance requirements.
5 Classification: small models Haiku/GPT-4o-mini Small models handle 80% of classification tasks at 1/10th the cost and latency: but you must understand their actual boundaries before choosing them.
6 Vision tasks: GPT-4.1, Gemini 2.5 Pro, Llama 4 Vision models have moved from research to production, but model choice depends on image resolution, latency budget, and whether you need reasoning or just classification.
7 Structured extraction: top performers Structured extraction is where AI proves immediate ROI in regulated industries: but only certain models handle the compliance and reliability constraints required.
5 Cost Optimization Strategy 7 lessons
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Routing: different models per task Route different tasks to different models based on cost, latency, and domain constraints: not every task needs GPT-4.1, and not every domain allows closed-source APIs.
2 Small models for simple tasks Small models (3B–7B parameters) solve 70% of business problems at 10% of the cost and latency of frontier models, but only if the task is genuinely simple.
3 Large models for complex tasks Large language models solve genuinely hard problems: document classification, contract analysis, clinical reasoning: but they cost 10–100x more per token than small models, so the business case requires either high-value outputs, regulatory requirements, or both.
4 Caching identical requests Caching prevents redundant API calls to the same model for identical inputs, cutting costs by 40–70% and latency by 80% in production systems.
5 Batch API discounts Batch APIs offer 50% cost savings for non-real-time workloads, but require fundamental changes to your architecture and latency expectations.
6 Open source for high-volume Open source models at scale require infrastructure investment upfront but eliminate per-token costs that become catastrophic at high volume.
7 Total cost of ownership The cheapest model on API pricing is almost never the cheapest model in production.
6 Evaluating Models for Your Use Case 7 lessons
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Benchmark vs real-task evaluation A model that scores 95% on a benchmark can fail catastrophically on your actual data: and you won't know until production hits.
2 Creating a Domain Test Set A domain test set is not a random sample of your data: it's a deliberate snapshot of the real-world conditions your model will face, built with your domain experts, not your data scientists alone.
3 Blind A/B evaluation A/B tests reveal model bias when neither evaluators nor data scientists know which model produced which output.
4 LLM-as-judge comparison LLM-as-judge (using an LLM to score outputs from another LLM) works well for preference rankings but fails catastrophically for objective correctness in regulated domains.
5 Human preference testing Human preference testing is how you validate that a model actually produces outputs humans want before you deploy it to real users.
6 Statistical Significance A model metric that looks good in isolation is worthless if you can't prove the improvement wasn't random luck.
7 Continuous re-evaluation Models degrade in production faster than you expect: you need systematic monitoring and governance, not just deployment.
7 Vendor Risk and Lock-in 7 lessons
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API Changes and Deprecations in Model Selection Selecting an AI model based on current API availability is a production debt guarantee: you must architect for API instability as your baseline assumption.
2 Price changes over time Model pricing changes continuously across vendors, APIs, and deployment modes: selecting a model locks you into cost assumptions that may not survive production.
3 Model updates changing behavior Model providers push updates that silently change inference behavior: you must version-lock models in production and test before upgrading.
4 Multi-provider strategy No single AI vendor owns your production system: architect for portability and redundancy from day one.
5 LiteLLM for abstraction LiteLLM is a vendor abstraction layer that lets you swap between competing LLM APIs without rewriting application code: critical when your preferred model is unavailable, too expensive, or blocked by compliance.
6 OpenAI-compatible APIs OpenAI-compatible APIs let you swap models without rewriting code: but you still have to choose which model to use, and that choice determines cost, latency, and reliability in ways the API abstraction hides.
7 Fallback configuration Every production model selection decision requires a documented fallback: what happens when your primary model fails, is unavailable, or produces unreliable output.