Visualisation

How much does an LLM charge per hour for its services?

If we multiple the Cost Per Output Token with Tokens Per Second, we can get the cost for what an LLM produces in Dollars Per Hour. (We’re ignoring the input cost, but it’s not the main driver of time.)

Over time, different models have been released at different billing rates. Most new powerful models like O3 or Gemini 2.5 Pro cost ~$7 – $11 per hr.

To get a sense of this, let’s look at wage rates across countries and industries:

Workers in Europe and Japan are already more expensive than the more expensive models, at $12+ per hour. India, Brazil, Mexico etc. are more expensive than most of the average models.

Once a language model’s run-time cost drops below the local minimum wage, the “offshoring” advantage disappears. AI becomes the cheapest employee in every country at once. Countries whose economies depend on being the “cheaper alternative” for labor-intensive work face potential economic disruption.

Paradoxically, workers in countries with strong labor protections, unions, and higher wages (like Germany and France) may paradoxically be safer from AI displacement.

Source code: sanand0/llmpricing
Analysis: ChatGPT

After seeing David McCandless‘ post “Which country is across the ocean?” I was curious which country you would reach if you tunneled below in a straight line (the antipode).

This is a popular visualization, but I wanted to see if I could get the newer OpenAI models to create the visual without me 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 any code (i.e. I just want the answer.) After a couple of iterations, O3 did a great job with this prompt:

𝙱𝚞𝚒𝚕𝚍 𝚊 _𝚜𝚒𝚗𝚐𝚕𝚎_ 𝙶𝚎𝚘𝙹𝚂𝙾𝙽 (𝙴𝙿𝚂𝙶:𝟺𝟹𝟸𝟼) 𝚝𝚑𝚊𝚝 𝚜𝚑𝚘𝚠𝚜, 𝚏𝚘𝚛 𝚎𝚊𝚌𝚑 𝚌𝚘𝚞𝚗𝚝𝚛𝚢, 𝚘𝚗𝚕𝚢 𝚝𝚑𝚎 𝚙𝚊𝚛𝚝𝚜 𝚘𝚏 𝚒𝚝𝚜 𝚊𝚗𝚝𝚒𝚙𝚘𝚍𝚎 𝚝𝚑𝚊𝚝 𝚕𝚒𝚎 𝚘𝚟𝚎𝚛 𝚘𝚌𝚎𝚊𝚗. 𝙲𝚊𝚛𝚎𝚏𝚞𝚕𝚕𝚢 𝚑𝚊𝚗𝚍𝚕𝚎 𝚌𝚘𝚞𝚗𝚝𝚛𝚒𝚎𝚜 𝚝𝚑𝚊𝚝 𝚜𝚝𝚛𝚊𝚍𝚍𝚕𝚎 𝚝𝚑𝚎 𝚙𝚛𝚒𝚖𝚎 𝚖𝚎𝚛𝚒𝚍𝚒𝚊𝚗 - 𝚄𝙺, 𝙵𝚛𝚊𝚗𝚌𝚎, 𝙰𝚕𝚐𝚎𝚛𝚒𝚊, 𝚎𝚝𝚌.

Here is the output and here is the ChatGPT conversation that generated it.

I learnt a few things:

1. Ask for the output, not the code. Models like O3 and O4 Mini can run code while thinking. Let’s stop asking for code to run. Just ask for the output directly. Let it figure out how.
2. Edge cases are everywhere. I had a problem with UK, France, Algeria, etc. straddling the prime meridian. If all goes well, you get AI-speed results. But it never does, and fixing it takes an expert and human-speed results. Programmers under-estimate edge cases, so compensate for this.

If you want to run this yourself, the code is at https://github.com/sanand0/antipodes

O3/O4 Mini are starting to replace Excel (or Tableau/Power BI) for quick analysis and visualizations. At least for me.

I normally open Excel when I need a fast chart or pivot. For instance, we track outages of our semi‑internal server, LLM Foundry.

To grab the data I ran one line in the browser console:

$$(".lh-base").map(d => d.textContent.trim()).filter(d => d.includes("From"))

This produced lines like:

Apr 20, 2025 03:11:27 PM +08 to Apr 20, 2025 03:27:12 PM +08 (15 mins 45 secs)
Apr 19, 2025 10:03:15 PM +08 to Apr 19, 2025 10:05:45 PM +08 (2 mins 30 secs)
Apr 19, 2025 09:47:13 PM +08 to Apr 19, 2025 09:49:45 PM +08 (2 mins 32 secs)
Apr 19, 2025 08:49:00 PM +08 to Apr 19, 2025 08:51:51 PM +08 (2 mins 51 secs)
Apr 19, 2025 08:13:02 PM +08 to Apr 19, 2025 08:15:35 PM +08 (2 mins 33 secs)
...

Then I told O4-Mini-High:

Here are downtimes for llmfoundry.straive.com.
Convert this to CSV and allow me to download it.
Also, draw the downtimes on a grid, rows=hour of day, columns=date,
cell contains 1 circle per outage in that time period,
size of each circle is based on the duration of the outage.

Apr 20, 2025 03:11:27 PM +08 to Apr 20, 2025 03:27:12 PM +08 (15 mins 45 secs)
Apr 19, 2025 10:03:15 PM +08 to Apr 19, 2025 10:05:45 PM +08 (2 mins 30 secs)
Apr 19, 2025 09:47:13 PM +08 to Apr 19, 2025 09:49:45 PM +08 (2 mins 32 secs)
… (rest of the data – about 50 rows)

Here’s the power of what a model like O4 Mini High can do.

1. It can reason. So, it planned an approach. (Convert to CSV, transform into date and hour. create a grid-based plot, use a pandas DataFrame, save it to a CSV, etc.)
2. It can code. It is pretty good at coding, and this is not too hard a problem, so it got the code right in one shot.
3. I can run code. This is a powerful step. It executed the code and produced the visualization above.

All of this took less than one minute.

I did not look at the code. I just focused on the picture and suggested changes.

This draws crosses, not circles, for each hour. Also, if there are multiple outages in an hour, I want multiple circles.

Here’s the output that took less than 10 seconds:

Next iteration:

Make the circles red with the same level of transparency.
Set the title to “LLM Foundry Downtime (SGT)”.
Instead of jittering the circle, let the Y position be the middle of the outage time.

Next iteration:

Change the red to a milder shade.
Set alpha to 0.5 but add a stroke with alpha 0.9.
Format the dates like “Sun 20 Apr”, etc.

That’s it! I never even looked at the code. The whole loop took 3 minutes – far faster than I could manage, though I’m good at code and data visualization!

More importantly, the model frees me to focus on the real problem, which is why is the downtime high?