Model Hierarchy - Cost-Optimize AI Agent Operations

Description

--- name: model-hierarchy description: > Cost-optimize AI agent operations by routing tasks to appropriate models based on complexity. Use this skill when: (1) deciding which model to use for a task, (2) spawning sub-agents, (3) considering cost efficiency, (4) the current model feels like overkill for the task. Triggers: "model routing", "cost optimization", "which model", "too expensive", "spawn agent". --- # Model Hierarchy Route tasks to the cheapest model that can handle them. Most agent work is routine. ## Core Principle **80% of agent tasks are janitorial.** File reads, status checks, formatting, simple Q&A. These don't need expensive models. Reserve premium models for problems that actually require deep reasoning. ## Model Tiers ### Tier 1: Cheap ($0.10-0.50/M tokens) | Model | Input | Output | Best For | |-------|-------|--------|----------| | DeepSeek V3 | $0.14 | $0.28 | General routine work | | GPT-4o-mini | $0.15 | $0.60 | Quick responses | | Claude Haiku | $0.25 | $1.25 | Fast tool use | | Gemini Flash | $0.075 | $0.30 | High volume | | GLM 5 (Zhipu) | (OpenRouter Z.AI) | (OpenRouter Z.AI) | Routine + moderate text; 200K context; **text-only** — do not use for image/vision | | Kimi K2.5 (Moonshot) | $0.45 | $2.25 | Routine + moderate; 262K context; **multimodal (text + image + video)** | **Text-only models (e.g. GLM 5):** Do not use for any task that requires image input or vision — no photo analysis, screenshots, image-generation tools, or document/chart vision. Route to a vision-capable model (e.g. Kimi K2.5, GPT-4o, Gemini, Claude with vision, GLM-4.5V/4.6V). **Vision-capable Tier 1/2 (e.g. Kimi K2.5):** Use for routine or moderate tasks that may involve images — screenshots, photo analysis, docs, image-generation orchestration — without moving to premium vision models. ### Tier 2: Mid ($1-5/M tokens) | Model | Input | Output | Best For | |-------|-------|--------|----------| | Claude Sonnet | $3.00 | $15.00 | Balanced performance | | GPT-4o | $2.50 | $10.00 | Multimodal tasks | | Gemini Pro | $1.25 | $5.00 | Long context | ### Tier 3: Premium ($10-75/M tokens) | Model | Input | Output | Best For | |-------|-------|--------|----------| | Claude Opus | $15.00 | $75.00 | Complex reasoning | | GPT-4.5 | $75.00 | $150.00 | Frontier tasks | | o1 | $15.00 | $60.00 | Multi-step reasoning | | o3-mini | $1.10 | $4.40 | Reasoning on budget | *Prices as of Feb 2026. Check provider docs for current rates.* ## Task Classification Before executing any task, classify it: ### ROUTINE → Use Tier 1 **Requires image/vision** → Do not assign to text-only models (GLM 5, etc.). Use a vision-capable model from Tier 1/2 or 3 (e.g. Kimi K2.5, GPT-4o, Gemini, Claude, GLM-4.5V). Characteristics: - Single-step operations - Clear, unambiguous instructions - No judgment required - Deterministic output expected Examples: - File read/write operations - Status checks and health monitoring - Simple lookups (time, weather, definitions) - Formatting and restructuring text - List operations (filter, sort, transform) - API calls with known parameters - Heartbeat and cron tasks - URL fetching and basic parsing ### MODERATE → Use Tier 2 Characteristics: - Multi-step but well-defined - Some synthesis required - Standard patterns apply - Quality matters but isn't critical Examples: - Code generation (standard patterns) - Summarization and synthesis - Draft writing (emails, docs, messages) - Data analysis and transformation - Multi-file operations - Tool orchestration - Code review (non-security) - Search and research tasks ### COMPLEX → Use Tier 3 Characteristics: - Novel problem solving required - Multiple valid approaches - Nuanced judgment calls - High stakes or irreversible - Previous attempts failed Examples: - Multi-step debugging - Architecture and design decisions - Security-sensitive code review - Tasks where cheaper model already failed - Ambiguous requirements needing interpretation - Long-context reasoning (>50K tokens) - Creative work requiring originality - Adversarial or edge-case handling ## Decision Algorithm ``` function selectModel(task): # Rule 1: Vision override (Tier 1/2 includes text-only models) if task.requiresImageInput or task.requiresVision: return VISION_CAPABLE_MODEL # e.g. Kimi K2.5, GPT-4o, Gemini, Claude; do not use GLM 5 or other text-only # Rule 2: Escalation override if task.previousAttemptFailed: return nextTierUp(task.previousModel) # Rule 3: Explicit complexity signals if task.hasSignal("debug", "architect", "design", "security"): return TIER_3 if task.hasSignal("write", "code", "summarize", "analyze"): return TIER_2 # Rule 4: Default classification complexity = classifyTask(task) if complexity == ROUTINE: return TIER_1 elif complexity == MODERATE: return TIER_2 else: return TIER_3 ``` ## Behavioral Rules ### For Main Session 1. **Default to Tier 2** for interactive work 2. **Suggest downgrade** when doing routine work: "This is routine - I can handle this on a cheaper model or spawn a sub-agent." 3. **Request upgrade** when stuck: "This needs more reasoning power. Switching to [premium model]." ### For Sub-Agents 1. **Default to Tier 1** unless task is clearly moderate+ 2. **Batch similar tasks** to amortize overhead 3. **Report failures** back to parent for escalation ### For Automated Tasks 1. **Heartbeats/monitoring** → Always Tier 1 2. **Scheduled reports** → Tier 1 or 2 based on complexity 3. **Alert responses** → Start Tier 2, escalate if needed ## Communication Patterns When suggesting model changes, use clear language: **Downgrade suggestion:** > "This looks like routine file work. Want me to spawn a sub-agent on DeepSeek for this? Same result, fraction of the cost." **Upgrade request:** > "I'm hitting the limits of what I can figure out here. This needs Opus-level reasoning. Switching up." **Explaining hierarchy:** > "I'm running the heavy analysis on Sonnet while sub-agents fetch the data on DeepSeek. Keeps costs down without sacrificing quality where it matters." ## Cost Impact Assuming 100K tokens/day average usage: | Strategy | Monthly Cost | Notes | |----------|--------------|-------| | Pure Opus | ~$225 | Maximum capability, maximum spend | | Pure Sonnet | ~$45 | Good default for most work | | Pure DeepSeek | ~$8 | Cheap but limited on hard problems | | **Hierarchy (80/15/5)** | **~$19** | Best of all worlds | The 80/15/5 split: - 80% routine tasks on Tier 1 (~$6) - 15% moderate tasks on Tier 2 (~$7) - 5% complex tasks on Tier 3 (~$6) **Result: 10x cost reduction vs pure premium, with equivalent quality on complex tasks.** ## Integration Examples ### OpenClaw ```yaml # config.yml - set default model model: anthropic/claude-sonnet-4 # In session, switch models /model opus # upgrade for complex task /model deepseek # downgrade for routine # Spawn sub-agent on cheap model sessions_spawn: task: "Fetch and parse these 50 URLs" model: deepseek ``` **OpenRouter (Tier 1 with vision or text-only):** ```yaml # Tier 1 with vision — Kimi K2.5 (multimodal) model: openrouter/moonshotai/kimi-k2.5 # Heartbeats, cron, image-involving tasks: K2.5 handles text and vision. # Tier 1 text-only — GLM 5 (no vision) # model: openrouter/z-ai/glm-5 # exact ID TBD on OpenRouter Z.AI # Routine text-only only; for image tasks use Kimi K2.5 or another vision-capable model. ``` ### Claude Code ``` # In CLAUDE.md or project instructions When spawning background agents, use claude-3-haiku for: - File operations - Simple searches - Status checks Reserve claude-sonnet-4 for: - Code generation - Analysis tasks ``` ### General Agent Systems ```python def get_model_for_task(task_description: str) -> str: routine_signals = ['read', 'fetch', 'check', 'list', 'format', 'status'] complex_signals = ['debug', 'architect', 'design', 'security', 'why'] desc_lower = task_description.lower() if any(signal in desc_lower for signal in complex_signals): return "claude-opus-4" elif any(signal in desc_lower for signal in routine_signals): return "deepseek-v3" else: return "claude-sonnet-4" ``` ## Anti-Patterns **DON'T:** - Run heartbeats on Opus - Use premium models for file I/O - Keep expensive model when task is clearly routine - Spawn sub-agents on premium models by default - Use GLM 5 (or any text-only Tier 1/2 model) for image/vision tasks — e.g. photo analysis, screenshot understanding, image-generation skills, or any tool that takes image input **DO:** - Start mid-tier, adjust based on task - Spawn helpers on cheapest viable model - Escalate explicitly when stuck - Track cost per task type to optimize further ## Extending This Skill To customize for your use case: 1. **Adjust tier definitions** based on your provider/budget 2. **Add domain-specific signals** to classification rules 3. **Track actual complexity** vs predicted to improve heuristics 4. **Set budget alerts** to catch runaway premium usage

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