aibot88 Model Routing
Choose the right model tier before starting a task. Overusing a capable model wastes cost and context. Underusing it produces lower quality on complex work.
When to Use
- Selecting a model for a new task or subagent
- Deciding whether to escalate after a failed attempt
- Designing a multi-agent pipeline with mixed task complexity
Tier Definitions (vendor-neutral)
| Tier | Typical examples | Task profile |
|---|---|---|
| Fast | Haiku, GPT-4o-mini, Gemini Flash | Mechanical, deterministic, narrow |
| Standard | Sonnet, GPT-4o, Gemini Pro | General implementation and review |
| Deep | Opus, o1, Gemini Ultra | Architecture, security, root-cause, release |
Use your provider's current recommended model for each tier. Do not hard-code model IDs in documentation or scripts; reference tiers instead.
Routing Table
Fast tier
Use when ALL of:
- Single file or single operation
- Output is deterministic (rename, format, classify, generate boilerplate)
- No ambiguity in the task description
- Low blast radius on failure (easy to retry or revert)
Examples: rename a variable, convert a data format, generate a changelog entry, classify issue severity.
Standard tier
Use when ANY of:
- Multi-file change with known scope
- Standard implementation task (add a feature, fix a bug, write tests)
- Code review of a bounded change
- Refactor with clear before/after contract
This is the default. When unsure, use Standard.
Deep tier
Use when ANY of:
- Architecture or system design decision
- Security audit or threat model
- Root-cause analysis with no clear reproduction
- Pre-release verification across a large surface
- Task requires reasoning across many files simultaneously
- Two Standard attempts failed with no clear progress
Do not use Deep speculatively. It is expensive and slower.
Escalation Rule
Try Standard first. Escalate to Deep only after Standard fails with a clear reasoning gap — not just a wrong answer. A wrong answer from Standard often means the task needs more context, not a more capable model.
Do not escalate because of anxiety about getting it right. Escalate because the attempt revealed a complexity that a smaller model cannot handle.
Session Discipline
- Start a new session after a major phase transition (research → implement → test)
- Compact context after finishing research before starting implementation
- Do not carry debugging traces into an implementation session
- Short-lived subagents default to Fast or Standard depending on task type
Cost Tracking
Record per task:
- Tier used
- Approximate token count
- Number of retries
- Success / escalated / failed
Use this to calibrate your routing decisions over time. If Standard succeeds > 90% of the time on a task type, that task does not need Deep.
Anti-Patterns
- Pre-escalation: Using Deep on a task that hasn't been tried at Standard
- Anxiety escalation: Using Deep because the task feels important, not because it's complex
- Under-routing: Using Fast for a multi-file refactor and then retrying three times
- Model pinning: Hard-coding specific model IDs in configs instead of tier references