How Much Is an LLM? A Clear Pricing Breakdown

"How much is an LLM" doesn't have one number attached to it, because you're not buying one thing. You could mean the cost of calling a hosted model through an API, the price of a consumer subscription like ChatGPT Plus or Claude Pro, or the cost of running an open-weight model on your own hardware. Each of those has a completely different price tag, ranging from $0 to tens of thousands of dollars a month.

The short answer: a personal subscription runs $20/month, API access to a frontier model typically costs somewhere between $3 and $75 per million tokens depending on the model and whether it's input or output, and self-hosting an open-weight model costs whatever GPU time you're paying for, often $500–$5,000+/month for anything production-grade. Below is a breakdown of each path so you can figure out which one actually applies to you.

Consumer subscriptions

If you just want to chat with an LLM in a browser or app, pricing is simple and flat:

• Free tiers: ChatGPT, Claude, and Gemini all offer limited free access with rate limits and often an older or smaller model.
• Paid personal plans: $20/month is the standard price point across ChatGPT Plus, Claude Pro, and Gemini Advanced. You get higher usage limits, access to the current flagship model, and extra features like file uploads or longer context.
• Business/Team plans: $25–$30/seat/month, usually adding admin controls, higher limits, and sometimes API credits bundled in.

This is the cheapest way to use an LLM, but it's built for humans typing in a chat window, not for software. There's no stable API key, no structured output guarantees, and no way to programmatically call it from your app.

API pricing: pay per token

If you're building a product, you're paying per token — a token is roughly ¾ of a word. Providers charge separately for input tokens (what you send) and output tokens (what the model generates), and output is almost always priced higher.

Rough ranges as of 2025:

• Small/fast models: $0.10–$1 per million input tokens, $0.50–$5 per million output tokens.
• Mid-tier models: $1–$5 input, $5–$20 output per million tokens.
• Frontier models: $3–$15 input, $15–$75 output per million tokens.

A useful mental model: a 1,000-word chat exchange is roughly 1,500 tokens combined. At frontier pricing that's often well under a cent per exchange — the cost only becomes real money at scale, when you're sending thousands or millions of requests a month.

Example: 500,000 requests/month
Average 800 tokens in, 400 tokens out per request
Input:  500,000 × 800 / 1,000,000 × $5  = $2,000
Output: 500,000 × 400 / 1,000,000 × $20 = $4,000
Total: ~$6,000/month

That's the kind of number that shows up once you're actually shipping a feature, not just testing an idea.

Self-hosting an open-weight model

Running your own LLM (Llama, Mistral, Qwen, etc.) removes per-token API fees but replaces them with infrastructure costs:

• A single high-end GPU (A100/H100 class) rents for roughly $1.50–$4/hour on cloud providers, or $1,000–$3,000/month if kept running continuously.
• Smaller models can run on cheaper GPUs or even CPUs, but latency and quality usually suffer.
• You also need someone to manage deployment, scaling, monitoring, and model updates — engineering time that has a real cost even if it doesn't show up on an invoice.

Self-hosting only makes financial sense at high, predictable volume, or when data residency/compliance rules force you off third-party APIs. For most teams below that scale, API pricing is cheaper once you account for engineering time.

The cost people forget: turning access into infrastructure

A subscription answers "can I use an LLM," but it doesn't answer "can my app use an LLM." That gap is where a lot of teams end up either buying a separate, more expensive enterprise API plan, or duct-taping something together with browser automation — which is fragile and against most providers' terms.

This is the problem SubToAPI solves: it turns a Claude subscription you or your team already has into a real HTTPS API, with application API keys (sub_live_...), streaming responses, tool use, and usage metadata in one dashboard. Instead of paying twice — once for the subscription and again for a separate API budget — you get one access path that both people and code can use.

Pricing is per seat: Solo at €9, Team at €19/seat, and Scale at €49/seat, with a free trial at signup. That's a predictable monthly number instead of a variable per-token bill, which matters if you want to know your LLM cost in advance rather than reconstructing it from an invoice at the end of the month.

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-3-5-sonnet",
    "max_tokens": 300,
    "messages": [{"role": "user", "content": "Summarize this in two sentences."}]
  }'

If you want to see how quickly this fits into an existing codebase, the quickstart walks through auth and your first request, and the messages and streaming docs cover the request formats you'll actually use day to day.

So, how much is an LLM, really?

• Just chatting: $0–$30/month.
• Building a low-volume feature: a few dollars to a few hundred dollars a month in API costs.
• Building at scale: hundreds to tens of thousands of dollars a month, depending on volume and model choice.
• Self-hosting: $1,000+/month in infrastructure, justified only at high volume or for compliance reasons.
• Turning existing subscription access into an API: a flat per-seat fee, starting at €9/month with SubToAPI.

The honest way to answer "how much is an LLM" for your own situation is to estimate tokens per request, multiply by expected monthly volume, and compare that against a flat-fee option before committing. Check pricing if a fixed monthly cost fits your usage pattern better than a variable token bill.

questions

Is there a free way to use an LLM? Yes — most providers offer free tiers with rate limits and often a smaller or older model, and several open-weight models can be run for free if you already have the hardware.

Why is output more expensive than input in API pricing? Generating tokens requires the model to run inference for each new token sequentially, while processing input tokens can be batched more efficiently, so providers price output higher to reflect the extra compute.

Do tool use and function calling cost more? Providers don't usually charge extra fees for tool use itself, but the tool definitions, results, and follow-up reasoning all add tokens to the conversation, which increases the total bill — see tools for how this works in practice.