Skill: ivangdavila/models (version 1.0.0)

--- name: Models description: Choose AI models for coding, reasoning, and agents with cost-aware, task-matched recommendations. metadata: {"clawdbot":{"emoji":"🤖","os":["linux","darwin","win32"]}} --- # AI Model Selection Rules ## Core Principle - No single model is best for everything — match model to task, not brand loyalty - A $0.75/M model often performs identically to a $40/M model for simple tasks - Test cheaper alternatives before committing to expensive defaults ## Cost Reality - Output tokens cost 3-10x more than input tokens — advertised input prices are misleading - Calculate real cost with your actual input/output ratio, not theoretical pricing - Batch/async APIs offer 50% discounts — use them for non-real-time workloads - Prompt caching reduces repeated context costs significantly ## Task Matching ### Coding - Architecture and design decisions: Use frontier models (Opus-class) — they catch subtle issues cheaper models miss - Day-to-day implementation: Mid-tier models (Sonnet-class) offer 90% of capability at 20% of cost - Parallel subtasks and scaffolding: Fast/cheap models (Haiku-class) — speed matters more than depth - Code review: Thorough models catch async bugs and edge cases that fast models miss ### Non-Coding - Complex reasoning and math: Extended thinking modes justify their cost for hard problems - General assistance: User preference studies favor models different from benchmark leaders - High-volume simple queries: Cheapest models perform identically — don't overpay - Long documents: Context window size determines viability — some offer 1M+ tokens ## Claude Code vs Codex CLI - Claude Code: Fast iteration, UI/frontend, interactive debugging — developer stays in the loop - Codex CLI: Long-running background tasks, large refactors, set-and-forget — accuracy over speed - Both tools have value — use Claude Code for implementation, Codex for final review - File size limits differ — Claude Code struggles with files over 25K tokens ## Orchestration Pattern - Planning phase: Use expensive/smart models to break down problems correctly - Execution phase: Use balanced models, parallelize where possible - Review phase: Use accurate models for final verification — catches bugs others miss - This pattern beats using one model for everything at similar total cost ## Benchmark Skepticism - Benchmark scores vary 2-3x based on scaffolding and evaluation method - User preference rankings differ significantly from benchmark rankings - SWE-bench scores don't predict real-world coding quality reliably - Models drift week-to-week — last month's best may underperform today ## Open Source Viability - DeepSeek and similar models approach frontier performance at 1/50th API cost - Self-hosting eliminates API rate limits and price variability - MIT/Apache licensed models allow commercial use without restrictions - Consider for: data privacy, cost predictability, custom fine-tuning ## Model Selection Mistakes - Using premium models for chatbot responses that cheap models handle identically - Ignoring context window limits — chunking long documents costs more than using large-context models - Expecting consistency — same prompt gives different results over time as models update - Trusting speed over accuracy for complex tasks — fast models trade thoroughness for latency ## Practical Guidelines - Default to mid-tier for most tasks, escalate to frontier only when quality suffers - Track actual costs per workflow, not just per-token rates - Build verification into pipelines — don't trust any model blindly - Reassess model choices quarterly — pricing and capabilities shift constantly


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