knowledge-base/wiki/tools/model-routing.md

created	updated	sources	tags
2026-07-07	2026-07-07	youtube/2026-07-07_berman-model-routing.md	tools model-routing cost-optimization routing-patterns fable planning-execution-split cross-model-calling not-diamond cursor-auto-mode copy-paste-routing coinbase glm-5.2 gpt-5.5

Model Routing — Cost-Saving Patterns

Source: Matthew Berman — "You NEED to do this right now..." (2026-07-06) See raw: ../../raw/youtube/2026-07-07_berman-model-routing.md

Core Principle

Route every task to the cheapest model that can handle it well. The single most impactful pattern is planning vs. execution separation: use frontier models for architecture/spec design, then delegate code execution to cheaper models.

Cost Comparison

Model	Input Cost / M Tokens	Output Cost / M Tokens	Role
Fable 5 (Anthropic)	$10	$50	Planning, Architecture, Specs
GPT 5.5	$2	$6	Code execution
Composer 2.5	$2	$6	Code execution
Claude Sonnet (mid)	$3	$15	Code execution
GLM 5.2 (Z.ai)	~$0.08	~$0.08	Open-source execution layer

Example: Fable-only = $9.50 baseline → Fable + GPT 5.5 routing = $6.48 (68% savings). Total potential with aggressive routing: >90%.

Routing Patterns

1. Manual Copy-Paste (~60% savings)

Developer manually copies code between models. No automation. Works as a proof-of-concept but doesn't scale.

2. Cross-Model-Calling (~70% savings)

One model calls another via API. A script/program orchestrates: Fable designs → API passes spec to GPT 5.5/GLM 5.2 → execution model writes code.

3. Cursor Auto Mode (~75% savings)

Cursor IDE's auto-mode feature routes tasks to the cheapest capable model automatically. Seamless developer experience.

4. Not Diamond (~80% savings)

Dedicated routing layer/API that classifies incoming requests and routes to the optimal model. Adds a routing decision layer between user and model.

5. OpenClaw Smart Model Router (see Skill)

[OpenClaw Smart Model Router skill] — Tier-based routing (5 tiers from simple to frontier). Claims 60-90% savings aligned with Berman's findings.

Enterprise Adoption

Coinbase is implementing model routing on open-source models, specifically GLM 5.2 (Z.ai). This validates:

• The routing pattern is production-grade, not experimental
• Chinese open-weight models (GLM 5.2) are viable as the cost-effective execution layer
• Enterprise security/compliance requirements can be met with routed open-source models

Key Rules

1. Separate planning from execution — Fable designs the architecture, cheap models write the code
2. Bounded specs produce reliable cheap execution — when the spec is tight, cheap models stay on track
3. Output tokens are the cost driver — frontier models charge $50/M output vs $6/M for cheap models
4. The pattern is simple — the hard part is building the discipline and the harness

Connection to Existing Wiki

• ../architecture/model-routing.md — OpenClaw's existing routing architecture (Two-Model-Pipeline, Fallback-Chain, GLM 5.2 support)
• ../concepts/llm/chinese-model-cost-routing.md — DeRonin's 87% cost-cut playbook (6 Western→Chinese swaps) complements Berman's patterns with field data
• ../concepts/llm/coding-benchmark-price-performance.md — atomic.chat benchmark: GLM 5.2 is B+ at $0.08 vs Fable 5 A+ at $3.12 (39× cheaper)
• ../concepts/llm/glm-5.2-zai-coding-model.md — Full GLM 5.2 documentation (1M context, MIT license, coding strength)
• ../concepts/llm/fable-5-anthropic.md — Fable 5 documentation (premium frontier)
• ../concepts/agents/subconscious-agent.md — OpenClaw's agent orchestration that could benefit from routing
• ../concepts/llm/llm-model-fusion-ensembles.md — Alternative pattern: parallel model panels instead of sequential routing
• ../concepts/prompt-caching.md — Complementary cost-reduction on the token level: cache stable prompt prefixes instead of routing models. Both can be combined (routed model + cached prefix).