Question 1

Which LLM provider is cheapest for my workload?

Accepted Answer

Cost depends on your token mix and quality bar. For balanced quality/cost, GPT-5.4 mini and Claude Sonnet 4.6 are common choices. For strict budget routing, GPT-4o-mini, Gemini 1.5 Flash, and GPT-5.4 nano can reduce spend. Run this calculator with real token distributions before deciding.

Question 2

How do LLM prices compare across providers?

Accepted Answer

A practical 2026 tiering view: premium quality includes GPT-5.4 and Claude Opus lines; balanced tier includes GPT-5.4 mini and Claude Sonnet 4.6; budget tier includes GPT-5.4 nano, GPT-4o-mini, Gemini Flash, and Claude Haiku families. Keep legacy models in your router for fallback and migration safety.

Question 3

Should I use multiple LLM providers?

Accepted Answer

Multi-provider strategy reduces risk and optimizes cost. Route simple tasks to budget models (Gemini Flash, Haiku), complex reasoning to premium (GPT-4, Claude Sonnet). Implement fallback chains. This approach typically saves 30-50% compared to single premium provider while maintaining quality.

Question 4

What hidden costs should I consider beyond token pricing?

Accepted Answer

Consider: API rate limits and retry costs, context caching fees, fine-tuning storage costs, embedding generation for RAG, network latency, error handling overhead, monitoring infrastructure, and compliance review time. These can add 10-25% to direct token costs in production.

Question 5

How does model selection affect response quality vs cost?

Accepted Answer

Higher-tier models (GPT-5.4, Claude Opus) are better for complex reasoning and high-stakes generation. Mid-tier models (GPT-5.4 mini, Claude Sonnet 4.6) cover most production workflows at lower cost. Budget models are best for extraction and classification. Evaluate with task-level acceptance tests, not benchmarks alone.

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