“Open-source AI is cheaper” is the biggest myth in 2025

A new study by Nous Research just flipped the popular narrative on its head: “open-source AI is cheaper” is no longer true once you count the actual tokens consumed.

For teams already running AI in production, this is a budget-exploding surprise.

Instead, closed-source APIs are quietly banking you money.

How?

1. Count the real cost = tokens × price-per-token
   • Nous Research just proved open-source burns 1.5-4× more tokens.
   • On simple Q&A, some models use 10× more tokens than o4-mini.
   → Your “free” model just cost you 10× the sticker price.

2. Closed providers compress reasoning traces
   • They run a tiny internal model to shrink the chain-of-thought.
   • You pay for 1 concise answer, not a 300-token monologue.

3. Open-source devs extend reasoning for accuracy
   • More steps = more tokens = more $.

4. 51 % of companies already run AI in prod
   • Inefficiency isn’t academic—it’s a runaway cloud bill.

Do this:

• Benchmark total cost per solved problem, not per token.

• Log prompt + completion sizes for a week.

• Switch high-traffic tasks to the most token-efficient model (often closed).

• Reserve open-source for fine-tuned or highly-custom workloads only.

Takeaway: Token discipline > model price tag.

The cheapest model is the one that answers in 3 words—not 300.