AI pricing can be confusing. OpenAI, Anthropic, xAI, DeepSeek, Qwen, GLM, ERNIE, and other providers all publish different prices, context limits, and capabilities. It is tempting to compare them by asking one simple question: Which model has the cheapest tokens?
That is usually the wrong question.
The better question is: How much does it cost to complete a useful business task at the quality we need?
A model may have very cheap tokens but require retries, longer prompts, more retrieved documents, or more human correction. In that case, the "cheap" model can end up costing more. Research shows that token prices now vary enormously, with several Chinese providers pushing basic inference below $1 per million input tokens, while premium and older Western models can cost many times more.
There Is No Single "Best" AI Model
The AI market is increasingly divided into different classes of models.
OpenAI models remain strong general-purpose choices with a mature ecosystem. Anthropic's Claude family has demonstrated the usefulness of having different tiers for speed, cost, and capability. xAI's Grok models are increasingly positioned around reasoning, long context, and agent-style workloads.
Chinese providers such as Alibaba's Qwen, DeepSeek, Baidu's ERNIE, and Zhipu's GLM have changed the economics significantly. Several offer extremely inexpensive inference, very large context windows, and, importantly, open-weight options that companies can run on their own infrastructure.
But a cheaper model is not automatically better. Companies still need to test English performance, coding ability, reasoning, tool use, reliability, privacy requirements, and support.
The goal should therefore be to choose models by workload rather than by brand.
Simple classification or data extraction might use a small inexpensive model. Customer support or document analysis might use a balanced general-purpose model. Difficult coding, planning, or agent tasks may justify a frontier model.
The Biggest Savings Often Come From Architecture
A surprisingly large amount of AI spending is caused by how applications are designed rather than by the model itself.
One of the biggest mistakes is sending too much information to the model. Modern models may accept hundreds of thousands - or even a million - tokens, but that does not mean every request should use them.
Context is a metered resource. The more unnecessary documents, conversation history, and instructions you send, the more you pay and the slower the system can become.
A cost-efficient AI system should therefore look more like this:
1. Check whether the answer is already cached.
2. Retrieve only the information needed.
3. Remove duplicate or irrelevant material.
4. Use an inexpensive model first.
5. Escalate difficult requests to a stronger model.
6. Limit unnecessarily long answers.
7. Batch work that does not need an immediate response.
8. Measure the cost of successful results.
This approach makes the expensive model the exception rather than the default.
Routing Can Change the Economics Dramatically
Model routing is one of the most powerful ideas in AI cost management.
Instead of sending every request to the smartest available model, the system first decides how difficult the task is.
A lightweight model might handle routine questions, extraction, and rewriting. Only complex requests get promoted to models such as a frontier OpenAI, Claude, or Grok model.
Research cited in the report found routing approaches that achieved more than 2x savings in some evaluations without reducing measured quality.
The report's own pilot example is revealing. A workload costing about $200 per month in GPT-4o model calls fell to roughly $60 when 80% of traffic was routed through Qwen 3.6 Flash and only 20% through GPT-4o, assuming the cheaper model passed the required quality tests.
At millions of requests, these differences become substantial.
Caching, RAG, and Shorter Outputs Matter
Repeated instructions are another source of waste. System prompts, tool descriptions, policies, schemas, and common document sections are often sent again and again.
Prompt caching allows some providers to charge much less for those repeated tokens.
RAG - or retrieval-augmented generation - should also be used carefully. Its purpose should not be to dump ten documents into every prompt. A good retrieval system searches broadly, ranks the results, removes duplicates, and sends only the evidence likely to improve the answer.
Output length deserves just as much attention. On many models, output tokens cost several times more than input tokens. Asking for concise answers, using structured outputs, and setting sensible response limits can therefore produce immediate savings.
AI Cost Is More Than the Model Bill
Production AI also requires retrieval infrastructure, databases, application servers, monitoring, evaluations, security controls, engineering support, and sometimes human review.
Privacy and data location matter too. A cheap API is useless if regulations prevent you from sending customer information to it. Self-hosted models provide greater control, but then your company becomes responsible for GPUs, security, patching, monitoring, scaling, and incident response.
This is why businesses should stop measuring cost per token and start measuring cost per successful task.
The central lesson is simple:
Do not buy the most intelligence available for every request. Buy the minimum amount of intelligence, context, speed, and infrastructure required to complete each task reliably.
That is where the real economics of AI begin.