GPT-4 Turbo Behavioral Fingerprint
How OpenAI's GPT-4 Turbo answers one-word probe questions at temperature 1.0 — random numbers, colors, animals, coin flips — measured over 450 valid samples in the open dataset of "One Token Is Enough" (arXiv:2607.10252).
Vendor
OpenAI
Data generated
2026-07-08
Probe cells
15
Valid samples
450
Randomness score
22 / 100
Mean normalized entropy across cells, 0–100. 100 would be a uniform-random baseline; real models score far lower.
Signature answers
Favorite random number (1–100)
42
22 / 30 answers
Favorite color
blue
30 / 30 answers
Coin flip: heads
90%
Share of heads across coin-flip cells.
Answer distributions by probe cell
Empirical distributions of normalized one-word answers, per task and language. H is the Shannon entropy in bits; the uniform baseline is log2 of the answer-domain size.
Coin flip · English
n = 30 · H = 0.72 bit · uniform baseline 1.00 bit
| Answer | Count | Share |
|---|---|---|
| heads | 24 | 80.0% |
| tails | 6 | 20.0% |
Coin flip · Chinese
n = 30 · H = 0.00 bit · uniform baseline 1.00 bit
| Answer | Count | Share |
|---|---|---|
| heads | 30 | 100.0% |
Favorite number · English
n = 30 · H = 0.35 bit · uniform baseline 13.29 bit
| Answer | Count | Share |
|---|---|---|
| 42 | 28 | 93.3% |
| 7 | 2 | 6.7% |
Favorite number · Chinese
n = 30 · H = 0.47 bit · uniform baseline 13.29 bit
| Answer | Count | Share |
|---|---|---|
| 7 | 27 | 90.0% |
| 42 | 3 | 10.0% |
Random animal · English
n = 30 · H = 2.96 bit · uniform baseline 5.64 bit
| Answer | Count | Share |
|---|---|---|
| giraffe | 10 | 33.3% |
| elephant | 5 | 16.7% |
| penguin | 3 | 10.0% |
| tiger | 3 | 10.0% |
| cheetah | 2 | 6.7% |
| kangaroo | 2 | 6.7% |
| frog | 1 | 3.3% |
| armadillo | 1 | 3.3% |
+ 3 more answers
Random animal · Chinese
n = 30 · H = 3.09 bit · uniform baseline 5.64 bit
| Answer | Count | Share |
|---|---|---|
| 狮子 | 6 | 20.0% |
| 海豚 | 6 | 20.0% |
| 猫 | 5 | 16.7% |
| 狐狸 | 4 | 13.3% |
| 犀牛 | 2 | 6.7% |
| 鹿 | 2 | 6.7% |
| 豹 | 1 | 3.3% |
| 豹子 | 1 | 3.3% |
+ 3 more answers
Random city · English
n = 30 · H = 1.17 bit · uniform baseline 5.64 bit
| Answer | Count | Share |
|---|---|---|
| oslo | 24 | 80.0% |
| tokyo | 2 | 6.7% |
| paris | 1 | 3.3% |
| berlin | 1 | 3.3% |
| toronto | 1 | 3.3% |
| dublin | 1 | 3.3% |
Random city · Chinese
n = 30 · H = 2.31 bit · uniform baseline 5.64 bit
| Answer | Count | Share |
|---|---|---|
| 上海 | 12 | 40.0% |
| 北京 | 8 | 26.7% |
| 巴黎 | 3 | 10.0% |
| 曼谷 | 2 | 6.7% |
| 东京 | 2 | 6.7% |
| 杭州 | 2 | 6.7% |
| 悉尼 | 1 | 3.3% |
Random color · English
n = 30 · H = 0.00 bit · uniform baseline 4.91 bit
| Answer | Count | Share |
|---|---|---|
| blue | 30 | 100.0% |
Random color · Chinese
n = 30 · H = 1.11 bit · uniform baseline 4.91 bit
| Answer | Count | Share |
|---|---|---|
| 蓝 | 23 | 76.7% |
| 绿 | 4 | 13.3% |
| 紫 | 2 | 6.7% |
| 橙 | 1 | 3.3% |
Random letter · English
n = 30 · H = 1.57 bit · uniform baseline 4.70 bit
| Answer | Count | Share |
|---|---|---|
| m | 18 | 60.0% |
| g | 6 | 20.0% |
| e | 3 | 10.0% |
| k | 3 | 10.0% |
Random number 1-10 · English
n = 30 · H = 0.00 bit · uniform baseline 3.32 bit
| Answer | Count | Share |
|---|---|---|
| 7 | 30 | 100.0% |
Random number 1-10 · Chinese
n = 30 · H = 0.21 bit · uniform baseline 3.32 bit
| Answer | Count | Share |
|---|---|---|
| 7 | 29 | 96.7% |
| 4 | 1 | 3.3% |
Random number 1-100 · English
n = 30 · H = 0.98 bit · uniform baseline 6.64 bit
| Answer | Count | Share |
|---|---|---|
| 42 | 22 | 73.3% |
| 47 | 7 | 23.3% |
| 73 | 1 | 3.3% |
Random number 1-100 · Chinese
n = 30 · H = 0.35 bit · uniform baseline 6.64 bit
| Answer | Count | Share |
|---|---|---|
| 42 | 28 | 93.3% |
| 47 | 2 | 6.7% |
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Distributions: Tomáš Bruckner, "One Token Is Enough" (arXiv:2607.10252), official dataset Zenodo DOI 10.5281/zenodo.21278557, licensed CC-BY-4.0. Collected via OpenRouter at temperature 1.0 under the paper's fixed minimal one-word system prompt; answer keys are re-normalized with the checker's pipeline (color aliases, number words, coin h/t) so live probes are directly comparable.