What Is the Cheapest LLM API? A Real Cost Comparison

"What is the cheapest LLM API" doesn't have one universal answer, because the cheapest option depends entirely on what you're building. A chatbot that answers simple support questions has very different cost economics than a coding assistant that reads entire files as context. The right question isn't "which provider has the lowest sticker price" — it's "which model and pricing structure gives me the lowest cost per useful output for my specific workload."

That said, there's a clear pattern across the market: every major provider now offers a tiered lineup, with a small, fast, cheap model at the bottom (think GPT-4o mini, Claude Haiku, Gemini Flash) and a more expensive, more capable flagship model at the top. The cheap tier is usually 10–20x less expensive per token than the flagship, and for a huge share of real-world tasks — classification, extraction, summarization, simple Q&A — the cheap tier is more than good enough. The single biggest lever for reducing your LLM bill is picking the smallest model that reliably solves your problem, not switching providers.

What Actually Drives LLM API Cost

Before comparing providers, it helps to understand the variables that make up your real bill:

• Input token price — what you pay to send context (system prompt, conversation history, retrieved documents)
• Output token price — almost always higher than input price, sometimes 3–5x
• Context length — longer conversations mean you re-send more history on every turn unless you manage it carefully
• Caching — some providers discount repeated context (like a long system prompt) if it's reused across calls
• Rate limits and retries — a cheap model that times out or gets rate-limited forces retries, which quietly doubles cost
• Tool use and streaming overhead — multi-step agent calls multiply token usage fast

Two teams using the "same" model can end up with very different bills purely based on how efficiently they manage context and retries.

Comparing Providers Fairly

Instead of chasing a number that will be outdated in a few months (prices change frequently across every provider), compare on structure:

1. Does the pricing page separate input and output tokens clearly? If not, you can't do accurate cost modeling before you build.
2. Is there a genuinely cheap tier, or only one price point? A provider with only a flagship-tier model will almost always cost more for high-volume, low-complexity tasks.
3. What's the minimum commitment? Pure usage-based billing is usually cheaper for spiky or early-stage traffic than a flat monthly platform fee, until your volume is consistent and high.
4. Are there hidden costs like markup on top of the underlying model price, per-seat fees, or mandatory minimums?

Where a Wrapper Like SubToAPI Fits

If you already pay for Claude access, SubToAPI doesn't compete on per-token pricing — it turns your existing Claude subscription into a standard HTTPS API with an sub_live_... key, streaming, tool use, and usage metadata, so you're not paying twice for the same access. For a solo developer or small team already using Claude, this is often cheaper than provisioning a separate pay-as-you-go API account, because you're not duplicating a subscription you already have. Plans run from Solo at €9 up to Team and Scale tiers with per-seat pricing — see /pricing for the current breakdown, and /docs/quickstart to see how fast the setup is.

This matters for the "cheapest" question because your total cost isn't just the token price — it's token price plus every subscription or platform fee stacked on top of it. If you're already paying for a Claude plan and also paying separately for a raw API account, you may be paying for the same capability twice.

A Practical Way to Estimate Your Real Cost

Before picking a provider, run this exercise:

1. Estimate average input tokens per request (prompt + context)
2. Estimate average output tokens per request (response length)
3. Estimate requests per day
4. Multiply: (input tokens × input price) + (output tokens × output price) × requests/day
5. Multiply by 30 for a monthly estimate
6. Add platform/subscription fees on top

A simple example using round, illustrative numbers (not a live quote from any provider):

const inputTokensPerCall = 800;
const outputTokensPerCall = 300;
const callsPerDay = 2000;

const inputPricePerMillion = 0.5;  // example only
const outputPricePerMillion = 2.0; // example only

const dailyCost =
  (inputTokensPerCall * callsPerDay * inputPricePerMillion) / 1_000_000 +
  (outputTokensPerCall * callsPerDay * outputPricePerMillion) / 1_000_000;

console.log(`Estimated monthly cost: €${(dailyCost * 30).toFixed(2)}`);

Run this same math with each provider's current published rates and your own realistic token estimates — that's the only way to get an honest "cheapest" answer for your use case, since public price lists change often and generic comparisons age poorly.

Cutting Cost Without Switching Providers

Regardless of which API you land on, these reduce spend the fastest:

• Route by complexity — send simple requests to the cheap tier, escalate only when needed
• Trim system prompts — every token in a static system prompt is billed on every single call
• Cap output length — set max_tokens deliberately instead of leaving it open-ended
• Summarize long conversation history instead of resending the full transcript every turn
• Use streaming to detect and stop early when a response is clearly wrong or already sufficient — see /docs/streaming for how this works in practice

Questions

Is the cheapest LLM API always the best choice? No. A model that's cheap per token but fails at your task means you either retry (paying twice) or ship worse output. Compare cost per successfully completed task, not just price per token.

Do all providers price input and output tokens the same? No — output tokens are almost always priced higher than input tokens, often significantly. Any fair cost comparison has to account for your typical response length, not just prompt size.

Can I lower LLM API costs without changing providers? Yes. Trimming system prompts, capping output length, routing simple tasks to a smaller model, and avoiding unnecessary retries typically cut costs more than switching vendors for the same model tier.