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- raw: raw/youtube/2026-07-07_berman-model-routing.md - wiki/tools/model-routing.md (new) — Cost-saving routing patterns - wiki/architecture/model-routing.md (update) — Berman patterns section - wiki/index.md — 67. Update with new tool + architecture update - wiki/log.md — changelog entry
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81 lines
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---
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type: youtube
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source_url: https://www.youtube.com/watch?v=1KKB_UiW6ls
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retrieved: 2026-07-07
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channel: "Matthew Berman"
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title: "You NEED to do this right now... — 90% less AI costs via Model Routing"
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duration_sec: 1113
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has_transcript: false
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tags: [model-routing, cost-optimization, fable, planning-execution-split, cross-model-calling, copy-paste-routing, cursor-auto-mode, not-diamond, genspark, coinbase, glm-5.2, gpt-5.5, composer-2.5, claude-sonnet, cost-savings, routing-strategy, audio-briefing, german]
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notebooklm: https://notebooklm.google.com/notebook/a5071bba-125f-4f08-86ea-99a1ab9911ca
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---
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# 90% less AI costs — Model Routing
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**Video URL:** [https://www.youtube.com/watch?v=1KKB_UiW6ls](https://www.youtube.com/watch?v=1KKB_UiW6ls)
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**NotebookLM:** [https://notebooklm.google.com/notebook/a5071bba-125f-4f08-86ea-99a1ab9911ca](https://notebooklm.google.com/notebook/a5071bba-125f-4f08-86ea-99a1ab9911ca)
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**Channel:** Matthew Berman (621K subscribers)
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**Published:** 2026-07-06
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**Duration:** 18:33 (18:46 Audio Briefing auf Deutsch)
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**Sponsor:** Genspark
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## Summary
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Matthew Berman demonstrates how Model Routing — the practice of sending different subtasks to different AI models based on cost and capability — can reduce AI costs by up to 90%. The core insight: frontier models like Fable (Anthropic, $10-50/M tokens) are overkill for most tasks; cheaper models (GPT 5.5 at $2/M, GLM 5.2 at $0.08/M, etc.) handle the majority of work within a bounded spec.
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## Key Points
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### 1. Planning vs. Execution Separation (Fable Pattern)
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**Fable (Anthropic)** is used for architecture and specification design (thinking/planning phase), then the actual code **execution** is handed off to cheaper models:
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- **GPT 5.5** ($2/M tokens input)
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- **Composer 2.5** (OpenAI code model)
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- **Claude Sonnet** (mid-tier Anthropic model)
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This is the single most impactful pattern: **split the expensive thinking from the cheap execution**.
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### 2. Dramatic Cost Differences
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| Model | Input Cost per M Tokens | Output Cost per M Tokens |
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|-------|----------------------|-----------------------|
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| Fable 5 (Anthropic) | $10 | $50 |
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| GPT 5.5 | $2 | $6 |
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| GLM 5.2 | ~$0.08 | ~$0.08 |
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| Cheap models (general) | $2 | $6 |
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**Example calculation:** Using Fable for everything costs $9.50 baseline. With routing to GPT 5.5 for execution: **$6.48 — a 68% savings.** Overall potential exceeds **90%** when routing aggressively.
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### 3. Routing Patterns Covered
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| Pattern | Description | Est. Savings |
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|---------|-------------|-------------|
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| **Manual Copy-Paste** | Developer manually copies code between models | ~60% |
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| **Cross-Model-Calling** | One model calls another model via API | ~70% |
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| **Cursor Auto Mode** | Cursor IDE auto-routes to cheapest model | ~75% |
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| **Not Diamond** | Dedicated routing layer/API | ~80% |
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### 4. Coinbase Example
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Coinbase is implementing model routing on **open-source models, specifically GLM 5.2** (Z.ai). This demonstrates enterprise adoption of the routing pattern with Chinese open-weight models as the cost-effective execution layer.
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### 5. Audio Briefing
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A **German-language audio briefing** (18:46 min) was generated from this video via NotebookLM, making the content accessible to German-speaking audiences.
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## Key Takeaways
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1. **Don't use one model for everything** — separate planning (expensive) from execution (cheap)
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2. **Fable is for architecture/specs**, not for writing boilerplate code
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3. **Routing can save 60-90%** with minimal quality loss when execution is well-specified
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4. **Multiple routing patterns exist** — from manual copy-paste to dedicated routing layers (Not Diamond)
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5. **Enterprise adoption** is happening (Coinbase on GLM 5.2)
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6. **The pattern is simple** but requires discipline to implement consistently
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## Relevance to Existing Wiki
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- **Cross-ref:** [[../../wiki/architecture/model-routing.md]] — OpenClaw's existing routing architecture
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- **Cross-ref:** [[../../wiki/concepts/llm/chinese-model-cost-routing.md]] — DeRonin's 87% cost-cut playbook (complementary data)
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- **Cross-ref:** [[../../wiki/concepts/llm/coding-benchmark-price-performance.md]] — Price-performance validation (39× cheaper GLM 5.2)
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- **Cross-ref:** [[../../wiki/concepts/llm/glm-5.2-zai-coding-model.md]] — GLM 5.2 details
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- **Cross-ref:** [[../../wiki/concepts/llm/fable-5-anthropic.md]] — Fable 5 details
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- **Cross-ref:** [OpenClaw Smart Model Router skill] — OpenClaw Smart Model Router skill (tier-based routing, 60-90% savings claim) |