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Hector 8f676acfa7 ingest(youtube): Matthew Berman - 90% less AI costs via Model Routing 
- 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
2026-07-07 10:31:37 +02:00

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type source_url retrieved channel title duration_sec has_transcript tags notebooklm
youtube https://www.youtube.com/watch?v=1KKB_UiW6ls 2026-07-07 Matthew Berman You NEED to do this right now... — 90% less AI costs via Model Routing 1113 false
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
https://notebooklm.google.com/notebook/a5071bba-125f-4f08-86ea-99a1ab9911ca

90% less AI costs — Model Routing

Video URL: https://www.youtube.com/watch?v=1KKB_UiW6ls NotebookLM: https://notebooklm.google.com/notebook/a5071bba-125f-4f08-86ea-99a1ab9911ca Channel: Matthew Berman (621K subscribers) Published: 2026-07-06 Duration: 18:33 (18:46 Audio Briefing auf Deutsch) Sponsor: Genspark

Summary

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.

Key Points

1. Planning vs. Execution Separation (Fable Pattern)

Fable (Anthropic) is used for architecture and specification design (thinking/planning phase), then the actual code execution is handed off to cheaper models:

  • GPT 5.5 ($2/M tokens input)
  • Composer 2.5 (OpenAI code model)
  • Claude Sonnet (mid-tier Anthropic model)

This is the single most impactful pattern: split the expensive thinking from the cheap execution.

2. Dramatic Cost Differences

Model Input Cost per M Tokens Output Cost per M Tokens
Fable 5 (Anthropic) $10 $50
GPT 5.5 $2 $6
GLM 5.2 ~$0.08 ~$0.08
Cheap models (general) $2 $6

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.

3. Routing Patterns Covered

Pattern Description Est. Savings
Manual Copy-Paste Developer manually copies code between models ~60%
Cross-Model-Calling One model calls another model via API ~70%
Cursor Auto Mode Cursor IDE auto-routes to cheapest model ~75%
Not Diamond Dedicated routing layer/API ~80%

4. Coinbase Example

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.

5. Audio Briefing

A German-language audio briefing (18:46 min) was generated from this video via NotebookLM, making the content accessible to German-speaking audiences.

Key Takeaways

  1. Don't use one model for everything — separate planning (expensive) from execution (cheap)
  2. Fable is for architecture/specs, not for writing boilerplate code
  3. Routing can save 60-90% with minimal quality loss when execution is well-specified
  4. Multiple routing patterns exist — from manual copy-paste to dedicated routing layers (Not Diamond)
  5. Enterprise adoption is happening (Coinbase on GLM 5.2)
  6. The pattern is simple but requires discipline to implement consistently

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