LLM Router: Cut AI Costs 80% with Multi-Model Orchestration

You are probably overpaying for AI. Not because you are bad at budgeting — because you are using a Ferrari to drive to the grocery store. Every time you send a simple customer support question to Claude Opus, every time you route a basic summarization task to GPT-4, every time you pay $15 per million tokens for work that a $0.50 model could do just as well — you are burning money.

The fix is not using cheaper models. The fix is routing — sending the right task to the right model. And doing it automatically.

This is what multi-model orchestration does. It is the difference between paying for every Uber ride and knowing when to take the bus.

Key Takeaways

Multi-model orchestration routes tasks between expensive and cheap AI models based on complexity — premium models for hard problems, budget models for simple ones.
The cost difference is massive: premium models ($10-75/1M tokens) vs. budget models ($0.10-3/1M tokens). That is a 50-100x price gap.
Real savings: teams report cutting AI bills by 70-85% with no measurable drop in quality for most tasks.
Four routing patterns: fan-out (parallel cheap workers), review chain (cheap writes, expensive reviews), escalation (start cheap, upgrade if complex), and cost-aware auto-routing.
The Multi-Model Orchestrator skill on Claw Mart gives you the complete framework — routing logic, model configs, and guardrails — for $9.

The Math That Will Make You Angry

Here is a simple exercise. Go pull your API logs for the last month. Categorize every request by complexity.

I will wait.

Back? Let me guess what you found. Somewhere between 60-80% of your requests are simple. Summarize this email. Rewrite this paragraph. Classify this feedback. Extract these names. The kind of task where the model does not need to reason deeply — it just needs to execute.

Now look at what you paid for those requests. If you are using premium models for all of them, here is what that looks like:

100M tokens/month on premium at $15/1M = $1,500/day = $45,000/month
Same 100M tokens routed correctly (75% budget, 25% premium) = $450/day = $13,500/month

Monthly savings: $31,500.

That is a full-time employee salary. For doing nothing more than sending the right task to the right model.

How Model Routing Works

The idea is simple. Analyze each request, determine complexity, pick the right model.

Simple-Shuffle: The most basic approach. Set weights for each model (e.g., 70% cheap, 30% premium), distribute requests accordingly. No analysis, just probability. Works if your workload is consistent.

Cost-Based Routing: More sophisticated. Analyze the request, predict the cheapest model that can handle it, route there. Requires more setup but saves more money.

Latency-Aware: If speed matters more than cost, route to the fastest available model regardless of price. Useful for real-time user-facing applications.

Escalation: Start with a budget model. If the output quality is insufficient or the model signals it is out of its depth, escalate to premium. This is the safest pattern — you get the savings by default but never sacrifice quality.

The escalation pattern is the one I recommend for most teams. Start cheap. Only upgrade when the budget model struggles. You get the savings without the risk.

Real Routing Patterns That Work

Here is how teams actually implement this:

The Fan-Out Pattern

Spawn multiple budget model agents in parallel to handle a task. Then use a premium model to aggregate and refine their outputs.

Example: You need to analyze 50 support tickets. Spawn 5 MiniMax agents to process 10 tickets each. Then feed all 5 outputs to Claude Opus for a final synthesis.

Total cost: (50 × $0.02) + ($0.10 for Opus synthesis) = $1.10 vs. $7.50 for Opus alone.

The Review Chain Pattern

Budget model writes the first draft. Premium model reviews and edits.

Example: Generate a first-pass email response with MiniMax. Then route to Claude Sonnet for tone check, fact verification, and polish.

Cost: $0.02 (draft) + $0.50 (review) = $0.52 vs. $1.50 for doing everything with premium.

The Escalation Pattern

This is the safest. Budget model attempts the task. If it fails confidence checks or produces low-quality output, route to premium.

Example: Run classification on MiniMax. If confidence < 80%, re-run on Sonnet. You only pay for premium when needed.

Cost-Aware Auto-Routing

The most automated. The router analyzes each request and picks the optimal model based on a cost-quality balance you define.

Tools like LiteLLM and OpenRouter handle this natively. You define rules (e.g., all summarization goes to MiniMax, all code review goes to Opus) and the router executes.

What Tasks Go Where

Not all tasks are equal. Here is a practical breakdown:

Budget models (MiniMax, Kimi, GPT-3.5) excel at:

Classification and categorization
Simple rewrite and rephrasing
Information extraction
Summarization of straightforward documents
Formatting transformations
Short-form content generation

Premium models (Opus, Sonnet, GPT-4) are worth the premium for:

Complex reasoning and analysis
Multi-step planning
Creative writing with specific tone
Technical architecture decisions
Anything involving ambiguity or judgment
Long-context understanding

The split is usually 70/20/10: 70% of requests to budget, 20% to mid-tier, 10% to premium. Adjust based on your specific workload.

The Tradeoffs

Routing is not free. Here is what you trade for those savings:

Latency overhead. The router needs time to analyze each request and decide where to send it. Expect 10-100ms additional latency depending on the routing strategy.

Setup complexity. Defining routing rules, configuring fallbacks, and monitoring quality takes initial work. This is not a plug-and-play solution — it requires tuning.

Quality risk. Budget models are genuinely worse at complex reasoning. If you route incorrectly, you get worse outputs. You need quality gates — either human review or automated confidence checks — to catch failures.

Monitoring requirements. You need to track not just costs but quality metrics. If routing saves money but degrades output quality, you have not saved anything.

The solution: start simple. Use the escalation pattern. Add complexity only after you have proven the baseline works.

Tools That Make This Easy

You do not need to build this from scratch. Several tools handle routing natively:

LiteLLM Router: The most popular open-source option. Supports 100+ models, multiple routing strategies, and sophisticated fallbacks. Good if you want full control.

OpenRouter Auto: Their auto-select feature analyzes prompts and routes to the optimal model from their catalog. No config needed — just set it and forget it.

NotDiamond: Claims 100x cost savings through dynamic routing. Uses prompt analysis to predict which model will perform best. Emerging technology, promising results.

All three integrate with OpenClaw. The Multi-Model Orchestrator skill includes configs for each.

The Bottom Line

If you are paying for premium AI models on every request, you are leaving massive savings on the table. Not because the technology is expensive — because you are using the wrong tool for most of your work.

The math is brutal but simple:

Before routing (100% premium at $15/1M): 100M tokens = $1,500/day = $45,000/month
After routing (75% budget at $1/1M, 25% premium at $15/1M): $450/day = $13,500/month

Monthly savings: $31,500. Annual savings: $378,000.

And that is a conservative split. Teams with workloads skewed toward simple tasks see even bigger gaps.

Your Next Steps

Here is what to do right now:

Pull your API logs this week. Categorize your top 10 request types by complexity. You will probably be surprised how many are simple.
Test two budget models against your simple tasks. Run 100 real examples through each. Score the outputs. You need data, not assumptions.
Grab the Multi-Model Orchestrator and set up cost-aware routing for your simplest task category first. Get one route working, prove the savings, then expand.
Set a 30-day goal: Route 50% of traffic to budget models with no measurable quality drop. That alone should cut your bill in half.

You do not need to route everything on day one. Start with the obvious wins — the high-volume, low-complexity requests that are eating your budget for no reason. Once those are running on cheap models and your users have not noticed, expand to the next category.

The money you are wasting on premium models for simple tasks is not coming back. But you can stop the bleeding today.