Cost per Action vs Cost per Token: Which LLM Pricing Model Fits Your Workflow?

When you use an LLM like GPT-4 or Claude 3, you’re not just asking a question-you’re paying for it. But how exactly are you being charged? Most companies think it’s simple: pay per word, or per request. But the real story is more complicated. Right now, nearly every LLM provider charges based on cost per token. But a new model is quietly rising: cost per action. And the difference between them could save you thousands-or cost you more than you expected.

What Even Is a Token?

Before you compare pricing models, you need to understand what a token is. It’s not a word. It’s not even always a full word. A token is a chunk of text that the model processes. In English, 1,000 tokens usually equals about 750 words. But it’s not consistent. "Hello" might be one token. "Unbelievable" could be two or three. "Dr. Smith"? Two tokens. "The cat sat on the mat."? Seven tokens. It depends on the model, the language, and even punctuation.

When you send a prompt to an LLM, it breaks it down into tokens. Then, when it generates a response, it builds that response one token at a time. Both the input and the output cost money. And they’re not priced the same.

Take Claude Sonnet 4.5. Input tokens cost $3 per million. Output tokens? $15 per million. That’s a five-to-one ratio. Why? Because generating new text is computationally heavier than reading what you typed. The model has to predict each next token, one after another, while input processing can happen in parallel. This isn’t a bug-it’s how the math works. But it means if your task generates long answers, your bill spikes fast.

And then there’s reasoning tokens. Some models, like Anthropic’s, charge extra for internal thinking. If the model has to reason through a legal contract step-by-step, those "thinking" steps are counted separately-and priced higher than output tokens. Users on Reddit have reported surprise bills 3x higher than expected because they didn’t realize the model was using extra tokens just to think.

Why Cost per Token Dominates (For Now)

Right now, 92% of LLM providers charge by token. That’s not random. It’s practical. Token-based pricing directly reflects the computational cost. More tokens? More GPU time. More memory. More electricity. Providers like OpenAI, Google, and Anthropic built their APIs around this model because it’s transparent, scalable, and fair-if you’re technically savvy.

For developers, it makes sense. You can track exactly how much each prompt costs. You can test different phrasings. You can cache responses. You can optimize prompts to cut token usage by 50% or more. A SaaS startup in Austin cut its monthly LLM bill from $1,200 to $380 just by rewriting prompts to be more concise. That’s real savings.

But here’s the catch: you need to be a developer to do this. You need to understand tokenization, context windows, caching, and model behavior. Most business teams don’t. Marketing teams don’t care how many tokens a blog post generates. Legal teams don’t want to count how many tokens are in a contract summary. They just want the job done.

Enter Cost per Action

Cost per action flips the script. Instead of charging for tokens, you charge for tasks. One contract review = $1. One summary = $0.50. One data extraction = $0.25. No counting. No surprises. Just a fixed price for a defined outcome.

This isn’t theoretical anymore. In April 2025, Jasper.ai launched "Content Generation Packs"-fixed pricing for one blog post, one social media campaign, or one product description. In May 2025, Harvey AI introduced "Legal Task Units"-$2.50 per contract review, $1.75 per clause extraction. These aren’t gimmicks. They’re responses to real pain.

The advantages are obvious:

• Budgets become predictable. Finance teams can plan.
• Non-technical users don’t need training. Just click "summarize" and pay the fee.
• Costs align with business value. You’re not paying for how long the model thinks-you’re paying for the result.

The downside? You might overpay. If a task takes 10 tokens instead of 50, you still pay the same. For simple, repeatable tasks, that’s fine. For complex ones, you lose flexibility.

Which Model Fits Your Use Case?

It’s not about which is better. It’s about which fits your workflow.

If you’re doing:

• Exploratory work (brainstorming, open-ended Q&A, research): Stick with cost per token. You need flexibility.
• High-volume, repetitive tasks (summarizing support tickets, extracting data from forms, classifying emails): Look for cost per action. You’ll save time and money.
• Long-form content generation (reports, whitepapers, legal briefs): Be careful. Per-token pricing can balloon if outputs are unpredictable. Per-action models with capped output length are safer.
• Enterprise workflows (HR onboarding, compliance checks, contract analysis): The future is hybrid. Providers are starting to offer "outcome-based pricing"-you pick the quality level (fast, balanced, thorough), and the system picks the model and pricing automatically.

Google’s Vertex AI Model Optimizer is already doing this. You say: "I need a summary with 95% accuracy, under 2 seconds." It picks the cheapest model that meets that. No token counting. Just results.

What’s Coming Next?

Per-token pricing isn’t going away. Not yet. But it’s becoming less relevant for non-technical users. By 2028, experts predict 35% of enterprise LLM workflows will use per-action or outcome-based pricing. Why? Because regulation is catching up. The EU AI Act now requires transparent cost calculation. That means providers can’t hide behind "it depends on tokens." They’ll need to show clear value per task.

Also, LLM inference costs are dropping fast. From Q3 2023 to Q1 2025, per-million-token costs fell by 68%. That’s a good thing. But it also means providers need new ways to make money. Charging by task lets them bundle value-like including editing, formatting, and compliance checks-into a single price.

The trend is clear: pricing is shifting from technical units to business outcomes. The best companies aren’t just choosing models-they’re choosing pricing models.

What Should You Do Today?

Start by asking three questions:

1. Do your teams understand token usage? If not, per-token pricing will confuse them.
2. Are your LLM tasks predictable? If yes, demand per-action pricing from your vendor.
3. Are you paying more than $100/month for LLMs? If so, you’re probably overpaying. Track usage. Test alternatives.

Tools like Traceloop and Langfuse can help you monitor token costs. But if your team doesn’t have engineers, don’t waste time optimizing tokens. Push your vendor for fixed pricing. Demand clarity. If they won’t give it, switch.

The future of LLM pricing isn’t about who has the cheapest tokens. It’s about who understands your work.