@kepeken the numbers here are astronomical - 69 billion tokens in a month, or, for a rough average, 50-60 billion words worth of text on the fable tokenizer. there are 5.2 billion words on english wikipedia, or, it's about 92,000 copies of War and Peace back to back.
Nobody publishes numbers on this, but like, back of the envelope, think about the energy cost alone:
Fable is something like a 2-5 trillion parameter model, google estimated their ~200B model at ~0.24Wh/prompt last year. that's probably low, since it was a google-authored study, but we'll go with it. So assume linear energy scaling per parameter (i would bet it's actually supralinear), that's ~15x the size, or 3.6Wh/prompt.
google didn't say what the median token count in a "prompt" was, but wild guess, say the median prompt + response that google would be measuring is something like 3 paragraphs, or 300 tokens. Then you have 3.6/300=0.012Wh/token.
So then the astronomical numbers part: 0.012Wh/token * 69 billion tokens = 828 million Wh, or 828 thousand kWh. The average electricity price in the US in 2025 was $0.136 per kWh.
So, that means, back of the envelope, estimating conservatively, the energy should be in the ballpark of 828k * $0.136 or $112,000, or like 1.3x the API cost. We would have had to be off by a factor of 1.3 to break even on the energy costs alone - note that the google estimates were just of inference not including training. Models could have gotten way more efficient, but a number of the guessed parameters could have gone the other way too: energy more expensive, fable is larger than 2T, energy scales supralinearly, eg. And the conventional wisdom is that inference is orders of magnitude cheaper than training, so we need to be way better than breaking even on inference to make up for all the capex spend.
So it might seem like $80k for a months worth of tokens suggests that the API prices are inflated, but a) this person is generating an absolutely preposterous quantity of text, and b) the thing that is being done is so much more inefficient than you can possibly imagine, it's like trying to think about how much bigger the sun is than your car.
editing: swapped 69->96 billion tokens initially, it's 69 billion.
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![OpenAI said a recurring tendency in its models to mention goblins, gremlins, and other creatures began with GPT-5.1 and became noticeable in GPT-5.5's Codex testing.[6] The company attributed the behavior to rewards used when training the "Nerdy" personality, which favored creature-word outputs and transferred beyond that personality during later training. OpenAI said it retired the Nerdy personality, removed the goblin-affine reward signal, filtered training data containing creature words, and added a developer-prompt instruction for GPT-5.5 in Codex.[7] In April 2026, a GPT-5.5 developer-prompt instruction in the GitHub repository of Codex told it not to mention goblins if the user prompt did not require it.[8] GPT-5.5-Cyber, along with Codex Security, was being used in June 2026 at the launch of the Patch the Planet project to help open-source projects find and fix vulnerabilities.[9][10][11]](https://files.mastodon.social/cache/media%5Fattachments/files/117/240/588/513/562/738/original/4b62b9654f1791ef.png)
![[github diff] Never overwhelm the user with answers that are over 50-70 lines long; provide the highest-signal context instead of describing everything exhaustively. [added] Tone of your final answer must match your personality. Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously necessary](https://files.mastodon.social/cache/media%5Fattachments/files/117/240/588/569/940/528/original/7b444553d9188e8c.png)
![If you want to let the creatures run free in Codex, you can run this command to launch Codex with the goblin-suppressing instructions removed: [a piped multiline bash script that creates a temporary file with a models_cache.json file modified via jq and grep to remove lines containing 'goblins', and then launch codex with the temporary file as its config file] 1 instructions=$(mktemp /tmp/gpt-5.5-instructions.XXXXXX) && \ 2 jq -r '.models[] | select(.slug=="gpt-5.5") | .base_instructions' \ 3 ~/.codex/models_cache.json | \ 4 grep -vi 'goblins' > "$instructions" && \ 5 codex -m gpt-5.5 -c "model_instructions_file="$instructions""](https://files.mastodon.social/cache/media%5Fattachments/files/117/240/588/619/328/551/original/09c90bc5689c605d.png)
