TinkerClaw Model Router

Model Router

Automatically select the right LLM for any task. Stop overpaying for simple tasks. Stop under-powering complex ones.

Quick Decision: Can You Route Without a Classifier?

Most tasks fit obvious categories. Check the Fast Route Table first. Only use the classifier for ambiguous cases.

Fast Route Table

Tier → Model Mapping

Configure these based on your available providers:

The "Good Enough" Principle

Not every task needs the smartest model. Most tasks need a fast, cheap, correct one.

Definitely Does NOT Need Big Brains (flash/fast tier)

These tasks have a single correct answer or a mechanical transformation. No model does them "better" — they all get it right. Use the cheapest:

• Date/time queries, timezone conversions
• Regex generation, string formatting
• JSON/CSV/XML transformations
• Template filling (mail merge, form letters)
• Data extraction from structured text
• Simple Q&A with context provided
• Spell checking, grammar fixes
• File listing, directory scanning summaries
• Status checks, health report formatting
• Translating short text

Needs Real Intelligence (mid tier)

These tasks benefit from a good model but don't need the frontier. The gap between mid and strong is <5% quality for 5x the cost:

• Code generation (functions, classes, modules)
• Code review and bug finding
• Content writing (blog posts, documentation)
• Email drafting with tone awareness
• Data analysis with narrative
• API integration code
• Test generation
• Summarizing long documents
• Morning briefings, daily reports

Actually Needs Top Tier (strong)

Only route here when the task genuinely requires deep reasoning or creativity that cheaper models measurably fail at:

• Multi-step debugging across files
• Architectural refactoring decisions
• Self-reflection and failure analysis (wind-down)
• Nuanced judgment calls (should we do X or Y?)
• Creative writing with specific voice/style
• Complex negotiation drafting
• Synthesizing contradictory information
• Tasks where being wrong has high cost

Classifier Prompt (For Ambiguous Cases)

When the Fast Route Table doesn't clearly match, use a cheap model to classify. Send this to gemini-flash or haiku:

Classify this task into exactly one tier. Reply with ONLY the tier name.

Tiers:
- flash: mechanical lookup, formatting, simple extraction
- fast: summarization, translation, template work
- mid: code generation, content writing, analysis, drafting
- strong: complex debugging, self-reflection, creative writing, architectural decisions
- reasoning: math proofs, formal logic, multi-step deduction

Task: {TASK_DESCRIPTION}

Tier:

Cost: 20 tokens ($0.000001). Negligible.

Generosity Rule (When in Doubt, Go Up)

If the classifier returns a tier but you're unsure:

• Non-critical task → trust the classifier
• User-facing output → go one tier up
• Irreversible action → always use strong
• Ambiguous between two tiers → pick the higher one

This is the "generous in doubt" principle: overspending 1¢ on a better model costs less than a bad result that needs re-doing.

Integration with OpenClaw

Sub-agent spawning

// Before (manual):
sessions_spawn({ task: "Review this PR", model: "sonnet" })

// After (auto-routed):
// 1. Check Fast Route Table → "Review code" → mid → sonnet
sessions_spawn({ task: "Review this PR", model: "sonnet" })

// For ambiguous tasks:
// 1. Fast Route doesn't match clearly
// 2. Send classifier prompt to gemini-flash
// 3. Get tier → map to model
// 4. Spawn with that model

Cron job model assignment

Use the table when creating or reviewing crons:

heartbeat:        flash  → qwen3 (local, free)
cleaning-lady:    fast   → sonnet
morning-briefing: mid    → sonnet
code review:      mid    → sonnet
wind-down:        strong → opus
self-evolution:   strong → opus

Agent-level rule (add to AGENTS.md)

## Model Routing

When spawning sub-agents, auto-select model by task type:
- Mechanical/extraction/formatting → gemini-flash
- Summarization/translation → haiku
- Coding/drafting/analysis → sonnet
- Deep reasoning/self-reflection → opus
- Math/logic/chain-of-thought → o3
When in doubt, go one tier up. Overpaying 1¢ beats re-doing work.

Provider Strengths (2026 Benchmarks)

For detailed model comparisons, see references/model-strengths.md.

Quick reference for tier selection when multiple models are available at the same tier:

Cron & Sub-Agent Routing

The router applies to ALL model selections, including:

• Sub-agents spawned by cron jobs (not just interactive)
• Sub-agents spawned by other sub-agents (recursive routing)
• Cron job model assignment at creation time
• The classifier model itself (always flash)

Cron Model Assignment

heartbeat:        flash/local  → qwen3 (free)
cleaning-lady:    fast         → haiku or sonnet
morning-briefing: mid          → sonnet
code review:      mid          → sonnet (or gpt for cross-model review)
wind-down:        strong       → opus (needs metacognition)
self-evolution:   strong       → opus
research reports: mid          → gemini (large context)

Sub-Agent Spawning Rule

When a cron job spawns sub-agents, EACH sub-agent gets its own tier:

Cron: morning-briefing (sonnet)
  └── Sub-agent: check emails → fast (haiku)
  └── Sub-agent: calendar summary → flash (gemini-flash)
  └── Sub-agent: draft briefing text → mid (sonnet)

Big Task Orchestration

For complex multi-step tasks, see references/task-orchestration.md:

• Hierarchical supervisor → workers pattern
• Pipeline pattern (gather → analyze → synthesize)
• Parallel fan-out with merge
• Context isolation to prevent collapse
• Claude Code architecture lessons (reverse-engineered)

Chain-of-Thought Optimization

Match CoT technique to tier for maximum ROI. See references/chain-of-thought.md:

• flash/fast: no CoT (tasks too simple)
• mid: structured CoT for complex sub-tasks
• strong: full CoT, Tree of Thought
• reasoning (o3): native CoT (don't prompt for it)

What to Keep in Bootstrap vs Auxiliary Files

In AGENTS.md (every prompt): Only the routing rule (7 lines):

When spawning sub-agents, auto-select model by task type:
- Mechanical/extraction/formatting → gemini-flash
- Summarization/translation → haiku
- Coding/drafting/analysis → sonnet
- Deep reasoning/self-reflection → opus
- Math/logic/chain-of-thought → o3
- Reviews/second opinions → gpt
When in doubt, go one tier up.

In this skill (loaded on demand): The full routing table, classifier prompt, tier definitions, provider strengths.

In reference files (loaded only when needed):

• references/model-strengths.md — detailed benchmarks and per-provider analysis
• references/task-orchestration.md — big task decomposition, Claude Code architecture
• references/chain-of-thought.md — CoT techniques matched to tiers

This follows the progressive disclosure principle: 7 lines always loaded, full skill on demand (~5KB), deep references only when the task requires them.

Anti-Patterns

• ❌ Using Opus for "what time is it" (flash task, 40x overspend)
• ❌ Using Flash for debugging a race condition (will miss it)
• ❌ Always defaulting to one model (defeats the purpose)
• ❌ Routing user-facing content to the cheapest model (quality matters)
• ❌ Classifying every task (most fit the Fast Route Table obviously)
• ❌ Putting the full routing table in bootstrap files (wastes tokens every prompt)
• ❌ Not routing cron sub-agents (they spend tokens too)
• ❌ Self-reviewing output (use a different model for review)

Pairs Well With

• model-prompt-adapter — once Router picks the model, Adapter fixes its quirks
• subagent-overseer — monitor the sub-agents you're routing models for
• agent-superpowers — the full engineering pipeline these routed agents should follow

👉 https://github.com/globalcaos/tinkerclaw

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