9d739dfa5d
- All gsdDir/gsdRoot/gsdHome → sfDir/sfRootDir/sfHome - GSDWorkspace* → SFWorkspace* interfaces - bootstrapGsdProject → bootstrapProject - runGSDDoctor → runSFDoctor - GsdClient → SfClient, gsd-client.ts → sf-client.ts - .gsd/ → .sf/ in all tests, docs, docker, native, vscode - Auto-migration: headless detects .gsd/ → renames to .sf/ - Deleted gsd-phase-state.ts backward-compat re-export - Renamed bin/gsd-from-source → bin/sf-from-source - Updated mintlify docs, github workflows, docker configs
94 lines
3 KiB
Text
94 lines
3 KiB
Text
---
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title: "Dynamic model routing"
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description: "Automatically select cheaper models for simple work and reserve expensive models for complex tasks."
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---
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Dynamic model routing classifies each dispatched unit into a complexity tier and selects an appropriate model. This reduces token consumption by 20-50% without sacrificing quality where it matters.
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The key rule: **downgrade-only semantics**. Your configured model is always the ceiling — routing never upgrades beyond what you've configured.
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## Enabling
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```yaml
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dynamic_routing:
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enabled: true
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```
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## Complexity tiers
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| Tier | Typical work | Default model level |
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|------|-------------|-------------------|
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| **Light** | Slice completion, UAT, hooks | Haiku-class |
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| **Standard** | Research, planning, execution | Sonnet-class |
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| **Heavy** | Replanning, roadmap reassessment | Opus-class |
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## Configuration
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```yaml
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dynamic_routing:
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enabled: true
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tier_models:
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light: claude-haiku-4-5
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standard: claude-sonnet-4-6
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heavy: claude-opus-4-6
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escalate_on_failure: true # bump tier on task failure
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budget_pressure: true # auto-downgrade near budget ceiling
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cross_provider: true # consider models from other providers
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```
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### `escalate_on_failure`
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When a task fails at a given tier, the router escalates: Light → Standard → Heavy. Prevents cheap models from burning retries on work that needs more reasoning.
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### `budget_pressure`
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Progressive downgrading as budget ceiling approaches:
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| Budget used | Effect |
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|------------|--------|
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| < 50% | No adjustment |
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| 50-75% | Standard → Light |
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| 75-90% | More aggressive |
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| > 90% | Nearly everything → Light |
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### `cross_provider`
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The router may select models from providers other than your primary, using a built-in cost table to find the cheapest model at each tier.
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## Task plan analysis
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For `execute-task` units, the classifier analyzes the task plan:
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| Signal | Simple → Light | Complex → Heavy |
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|--------|---------------|----------------|
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| Step count | ≤ 3 | ≥ 8 |
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| File count | ≤ 3 | ≥ 8 |
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| Description length | < 500 chars | > 2000 chars |
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| Code blocks | — | ≥ 5 |
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| Complexity keywords | None | Present |
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## Adaptive learning
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The routing history (`.sf/routing-history.json`) tracks success/failure per tier per unit type. If a tier's failure rate exceeds 20%, future classifications are bumped up.
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User feedback (`/sf rate`) is weighted 2x vs automatic outcomes.
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## Cost table
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| Model | Input (per M) | Output (per M) |
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|-------|-------|--------|
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| claude-haiku-4-5 | $0.80 | $4.00 |
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| claude-sonnet-4-6 | $3.00 | $15.00 |
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| claude-opus-4-6 | $15.00 | $75.00 |
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| gpt-4o-mini | $0.15 | $0.60 |
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| gpt-4o | $2.50 | $10.00 |
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| gemini-2.0-flash | $0.10 | $0.40 |
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The cost table is for comparison only — actual billing comes from your provider.
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## Interaction with token profiles
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- **Token profiles** control phase skipping and context compression
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- **Dynamic routing** controls per-unit model selection within those constraints
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The `budget` profile + dynamic routing provides maximum cost savings.
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