AI + Technology
Using a single model for every task is like using a single tool for every job. The wrong selection doesn't just cost money — it either overpays for quality you don't need or delivers quality too low for the task.
GPT-4o, Claude 3.5 Sonnet, Claude 3 Opus
Complex reasoning, creative generation, multi-step code, vision, ambiguous tasks where quality failure is expensive
GPT-4o mini, Claude 3 Haiku, Gemini 1.5 Pro
Structured extraction, summarization, moderate complexity Q&A, classification with nuance
Gemini 1.5 Flash, Llama 3 (Groq), Claude Haiku
High-volume classification, routing, simple chat, intent detection, batch async jobs
1. What is the cost of a quality failure?
If a bad output reaches a user and causes churn, support tickets, or wrong decisions — use frontier. If a bad output is easily caught or has low stakes — use budget tier.
2. What is the latency requirement?
Real-time chat needs sub-2s response. Budget and mid models are faster. Frontier models add 2–5s. Match model to acceptable latency.
3. What is the token volume?
At low volume, model cost difference is trivial. At 1M+ calls/day, the difference between tiers is hundreds of thousands of dollars annually.
4. Is this a well-defined or ambiguous task?
Well-defined tasks with structured outputs (extract, classify, format) don't benefit from frontier reasoning. Ambiguous synthesis tasks do.
A routing layer classifies each incoming request before LLM selection. The classifier itself should use a fast, cheap model. The routing logic can be rules-based, ML-based, or a small LLM call.
// Pseudo-code routing example
if complexity === 'low' and task in ['classify', 'extract']:
model = 'gpt-4o-mini'
elif task in ['summarize', 'moderate_qa']:
model = 'claude-3-haiku'
else:
model = 'claude-3-5-sonnet'
Context window
Claude models lead on context length (200k). Critical for document processing.
Instruction following
GPT-4o and Claude 3.5 Sonnet lead on structured output adherence (JSON, XML).
Code generation
GPT-4o leads on HumanEval benchmarks. Claude competitive on real-world codebases.
Safety / refusals
Claude models more conservative. May refuse ambiguous requests. Consider for user-facing products.
Rate limits
OpenAI higher default throughput. Anthropic/Google need tier upgrades for production volume.