What Is an AI Model Router?
An AI model router is a layer that sits in front of your LLM API calls and decides, for each query, which model is the best fit — balancing cost, speed, and quality. Instead of routing 100% of your traffic to the most capable (and most expensive) model, a router classifies the complexity of each request and sends simple queries to a cheaper, faster model while reserving the expensive model for the tasks that actually need it.
The fundamental insight behind routing is that most real-world AI applications have a highly uneven query distribution. A customer service chatbot might receive 70% simple factual questions and only 30% complex queries requiring reasoning. An AI writing assistant might generate 65% of its outputs with straightforward summarisation and 35% with nuanced creative generation. Routing exploits this distribution to cut costs without any visible quality change for end users.
Why Developers Overspend on LLMs
The default path for most development teams is to choose a single capable model — GPT-4o, Claude Sonnet, or Gemini Pro — and route everything to it. This is fast to implement and guarantees quality, but it is extremely expensive at scale. At 10,000 daily calls with 2,000 tokens each, sending everything to GPT-4o costs approximately $1,800/month. The same workload with a 60/40 router costs closer to $700/month — a saving of over $1,100 every month.
The core problem is that teams optimise for quality during development (when volumes are low and costs are negligible) and never revisit model selection when they scale. By the time the bill is noticeable, the routing architecture requires a refactor that no one has time for. The result is paying Tier 1 prices for Tier 3 queries indefinitely.
How LLM Pricing Works
Every major LLM provider charges per token — the basic unit of text (approximately 0.75 words). Pricing is split between input tokens (the prompt, context, and conversation history you send) and output tokens (the response the model generates). Output tokens are consistently more expensive than input tokens, often by 3–5×, because generation requires more compute than processing.
This calculator estimates costs by assuming 30% input tokens and 70% output tokens — a common distribution for conversational and agentic applications. If your application is primarily document processing (more input) or short-form generation (more output), your actual costs may differ. Check your provider's usage dashboard for your exact input/output split.
Model Pricing Reference (May 2025)
Prices shown as USD per 1 million tokens.
| Model | Input ($/1M) | Output ($/1M) | Best for |
|---|---|---|---|
| GPT-4o | $2.50 | $10.00 | Complex reasoning, code, nuanced generation |
| GPT-4o mini | $0.15 | $0.60 | Classification, Q&A, short summaries, extraction |
| Claude Opus 4.5 | $15.00 | $75.00 | Highest-complexity reasoning, agentic tasks |
| Claude Sonnet 4.5 | $3.00 | $15.00 | Balanced quality + cost, coding, analysis |
| Claude Haiku 3.5 | $0.80 | $4.00 | Fast, cheap, strong on structured tasks |
| Gemini 1.5 Pro | $1.25 | $5.00 | Long context, multimodal, document tasks |
| Gemini 1.5 Flash | $0.075 | $0.30 | Fastest, cheapest, best cost-to-performance ratio |
| Llama 3.1 70B (Groq) | $0.59 | $0.79 | Low latency, open-source, no output premium |
Note: Prices subject to change. Verify with each provider before budgeting.
Building a Simple LLM Router in Practice
The simplest router is a rule-based classifier that runs before each LLM call. It examines the query and assigns it to a "simple" or "complex" bucket based on heuristics:
- Word count: Queries under 50 words are often simple; over 200 words suggest complex context.
- Reasoning keywords: Words like "explain", "compare", "analyse", "why", "design", and "debug" signal complex queries.
- Expected output length: If the user asks for a one-sentence answer, any capable model will do. If they need a 1,000-word analysis, you want the best model.
- Query type: Classification, extraction, and translation tasks are almost always simple. Summarisation of long documents, code review, and creative writing are complex.
More sophisticated routers use a tiny classifier model (itself very cheap) to score complexity, or fine-tune a small model specifically on your query distribution to maximise routing accuracy. Companies like Martian offer drop-in API routing that handles classification automatically.
Expected Savings by Query Volume
Routing savings scale linearly with volume. Here are example estimates using GPT-4o (current model) routed to GPT-4o mini (60% of queries), at 2,000 tokens per call:
- 1,000 calls/day: ~$180/month → ~$70/month after routing. Saves $110/month.
- 5,000 calls/day: ~$900/month → ~$350/month after routing. Saves $550/month.
- 10,000 calls/day: ~$1,800/month → ~$700/month after routing. Saves $1,100/month.
- 50,000 calls/day: ~$9,000/month → ~$3,500/month after routing. Saves $5,500/month.
Even at 1,000 daily calls, the annual saving from a router exceeds $1,300 — often worth more than the engineering time to implement one. At 10,000 calls per day, the saving funds a full-time developer.
When NOT to Use a Router
Routing adds latency and complexity. It is not worth implementing if: (1) your total monthly LLM bill is under $100 and unlikely to grow; (2) your query mix is already dominated by complex tasks (less than 20% simple queries reduces the saving significantly); (3) your application is latency-critical and the extra classification step would degrade user experience; or (4) you have strict compliance requirements that limit which models can process data and all approved models are similarly priced.
For most growing AI applications processing more than 2,000 queries per day, however, routing is one of the highest-ROI optimisations available — faster to implement than most feature work and immediately impactful on unit economics.