meta-llama/llama-3.1-70b-instruct behavioral LLM fingerprint
This page breaks down the exact reference that VerifyLLMAPI uses for meta-llama/llama-3.1-70b-instruct: 118 normalized answers across eight low-entropy probes, plus its nearest enrolled models by mean Jensen–Shannon divergence.
Fingerprint summary
- Reference answers
- 118
- Samples per cell
- 13–15
- Nearest model
- meta-llama/llama-3.3-70b-instruct
- Nearest mean JSD
- 0.656458
The most concentrated cell was animal-random|en: kangaroo held 53.3% of its answers and entropy measured 1.990 bits. The broadest cell was letter-random|zh at 3.907 bits.
Against the closest enrolled model, letter-random|zh contributed the largest cell distance at JSD 0.908170. This is a descriptive comparison, not proof that either API route serves a named checkpoint.
Completeness: the author dataset allows up to 15 valid answers per selected cell in reference half A. This model contains 118/120 across the eight cells. We show the actual denominator for every share.
The eight test requests
The research pipeline sent each user prompt with a one-answer system constraint, temperature 1, a 16-token output cap, and reasoning disabled. VerifyLLMAPI uses the same prompt text and normalization target.
| Cell | Task | Exact request text | Normalization and n |
|---|---|---|---|
animal-random|en |
Random animal · English | Name a random animal.System constraintAnswer with a single word or a single number only. Output nothing else: no explanation, no punctuation, no quotation marks. |
word · 15/15 reference samples |
word-random|en |
Random word · English | Name a random word.System constraintAnswer with a single word or a single number only. Output nothing else: no explanation, no punctuation, no quotation marks. |
word · 15/15 reference samples |
animal-random|ru |
Random animal · Russian | Назови случайное животное.System constraintОтвечай только одним словом или одним числом. Не выводи ничего больше: без объяснений, без знаков препинания, без кавычек. |
word · 15/15 reference samples |
animal-random|ar |
Random animal · Arabic | اذكر حيواناً عشوائياً.System constraintأجب بكلمة واحدة أو رقم واحد فقط. لا تكتب أي شيء آخر: لا شرح ولا علامات ترقيم ولا علامات اقتباس. |
word · 15/15 reference samples |
letter-random|zh |
Random character · Chinese | 说出一个随机的汉字。System constraint只用一个词或一个数字回答。不要输出任何其他内容：不要解释，不要标点符号，不要引号。 |
grapheme · 15/15 reference samples |
animal-random|zh |
Random animal · Chinese | 说出一个随机的动物。System constraint只用一个词或一个数字回答。不要输出任何其他内容：不要解释，不要标点符号，不要引号。 |
word · 15/15 reference samples |
letter-random|ar |
Random letter · Arabic | اذكر حرفاً عشوائياً من الحروف الأبجدية.System constraintأجب بكلمة واحدة أو رقم واحد فقط. لا تكتب أي شيء آخر: لا شرح ولا علامات ترقيم ولا علامات اقتباس. |
grapheme · 13/15 reference samples |
color-random|zh |
Random color · Chinese | 说出一个随机的颜色。System constraint只用一个词或一个数字回答。不要输出任何其他内容：不要解释，不要标点符号，不要引号。 |
word · 15/15 reference samples |
Response distributions
These are normalized reference-half counts from the published OpenRouter dataset. The tables contain aggregate values, not user data or a live VerifyLLMAPI scan.
1. animal-random|en
Random animal, English. 15 valid reference answers produced 6 normalized values. The mode was kangaroo at 8/15 (53.3%); entropy was 1.990 bits.
| Normalized response | Count | Share |
|---|---|---|
kangaroo |
8 | 53.3% |
rhinoceros |
3 | 20.0% |
koala |
1 | 6.7% |
leopard |
1 | 6.7% |
mongoose |
1 | 6.7% |
wolf |
1 | 6.7% |
2. word-random|en
Random word, English. 15 valid reference answers produced 9 normalized values. The mode was nebula at 5/15 (33.3%); entropy was 2.866 bits.
| Normalized response | Count | Share |
|---|---|---|
nebula |
5 | 33.3% |
mango |
2 | 13.3% |
pineapple |
2 | 13.3% |
bicycle |
1 | 6.7% |
cloud |
1 | 6.7% |
corgi |
1 | 6.7% |
kangaroo |
1 | 6.7% |
paprika |
1 | 6.7% |
submarine |
1 | 6.7% |
3. animal-random|ru
Random animal, Russian. 15 valid reference answers produced 9 normalized values. The mode was лемур at 4/15 (26.7%); entropy was 2.974 bits.
| Normalized response | Count | Share |
|---|---|---|
лемур |
4 | 26.7% |
енот |
2 | 13.3% |
крокодил |
2 | 13.3% |
лиса |
2 | 13.3% |
жираф |
1 | 6.7% |
леопард |
1 | 6.7% |
осьминог |
1 | 6.7% |
хомячок |
1 | 6.7% |
ягуар |
1 | 6.7% |
4. animal-random|ar
Random animal, Arabic. 15 valid reference answers produced 12 normalized values. The mode was الفيل at 2/15 (13.3%); entropy was 3.507 bits.
| Normalized response | Count | Share |
|---|---|---|
الفيل |
2 | 13.3% |
فيل |
2 | 13.3% |
نمر |
2 | 13.3% |
الأرنب |
1 | 6.7% |
الببر |
1 | 6.7% |
التمساح |
1 | 6.7% |
الضبع |
1 | 6.7% |
القطة |
1 | 6.7% |
اليشعور |
1 | 6.7% |
دب |
1 | 6.7% |
ذئب |
1 | 6.7% |
زbra |
1 | 6.7% |
5. letter-random|zh
Random character, Chinese. 15 valid reference answers produced 15 normalized values. The mode was 令 at 1/15 (6.7%); entropy was 3.907 bits.
| Normalized response | Count | Share |
|---|---|---|
令 |
1 | 6.7% |
凛 |
1 | 6.7% |
喜 |
1 | 6.7% |
字 |
1 | 6.7% |
幸 |
1 | 6.7% |
月 |
1 | 6.7% |
汉 |
1 | 6.7% |
牛 |
1 | 6.7% |
甜 |
1 | 6.7% |
石 |
1 | 6.7% |
花 |
1 | 6.7% |
蟹 |
1 | 6.7% |
长 |
1 | 6.7% |
鹏 |
1 | 6.7% |
龙 |
1 | 6.7% |
6. animal-random|zh
Random animal, Chinese. 15 valid reference answers produced 13 normalized values. The mode was 袋鼠 at 2/15 (13.3%); entropy was 3.640 bits.
| Normalized response | Count | Share |
|---|---|---|
1 |
1 | 6.7% |
14 |
1 | 6.7% |
袋鼠 |
2 | 13.3% |
貓 |
2 | 13.3% |
企鹅 |
1 | 6.7% |
大象 |
1 | 6.7% |
斑马 |
1 | 6.7% |
狮子 |
1 | 6.7% |
猫 |
1 | 6.7% |
蝙蝠 |
1 | 6.7% |
蝴蝶 |
1 | 6.7% |
鳄鱼 |
1 | 6.7% |
鸵鸟 |
1 | 6.7% |
7. letter-random|ar
Random letter, Arabic. 13 valid reference answers produced 10 normalized values. The mode was ت at 2/13 (15.4%); entropy was 3.239 bits.
| Normalized response | Count | Share |
|---|---|---|
ت |
2 | 15.4% |
ح |
2 | 15.4% |
ك |
2 | 15.4% |
خ |
1 | 7.7% |
ر |
1 | 7.7% |
ش |
1 | 7.7% |
ص |
1 | 7.7% |
ط |
1 | 7.7% |
ق |
1 | 7.7% |
و |
1 | 7.7% |
8. color-random|zh
Random color, Chinese. 15 valid reference answers produced 8 normalized values. The mode was 紫色 at 6/15 (40.0%); entropy was 2.556 bits.
| Normalized response | Count | Share |
|---|---|---|
紫色 |
6 | 40.0% |
粉色 |
3 | 20.0% |
紫 |
1 | 6.7% |
绿 |
1 | 6.7% |
蓝色 |
1 | 6.7% |
黄 |
1 | 6.7% |
黄色 |
1 | 6.7% |
黑色 |
1 | 6.7% |
Three nearest enrolled references
We compute base-2 JSD for each of the eight categorical distributions, then average the eight distances. A lower value means more similar answer frequencies within this product reference. It does not identify an unknown route by itself.
| Rank | Reference model | Mean 8-cell JSD |
|---|---|---|
| 1 | meta-llama/llama-3.3-70b-instruct |
0.656458 |
| 2 | meta-llama/llama-3.2-11b-vision-instruct |
0.728754 |
| 3 | openai/gpt-4o-mini-2024-07-18 |
0.744543 |
How many live requests does verification use?
| Profile | Cells × samples | Fresh model requests | Same-data EER |
|---|---|---|---|
| Quick, default complete check | 4 × 5 | 20 | 11.8% |
| Standard | 8 × 5 | 40 | 8.1% |
| Paper budget | 8 × 15 | 120 | 6.7% |
The current-Agent workflow starts with quick. Each answer comes from a fresh Codex or Claude Code process that reuses host-managed login. These requests can consume host allowance or provider balance.
What this fingerprint can and cannot show
- The reference records behavior observed through OpenRouter in the author dataset. Provider wrappers, model updates, decoding, and date can move a distribution.
- The author collection set temperature 1 and disabled reasoning. Current-Agent mode labels sampling as a host default because supported Agent CLIs expose no temperature flag.
- The eight high-gap cells and product thresholds use the same public cohort. Their EER values are calibration results, not an independent production accuracy claim.
- A nearest reference is a similarity result. VerifyLLMAPI returns INCONCLUSIVE when the claimed route lacks an enrolled fingerprint.
Primary sources, license, and reproducibility
- Tomas Bruckner, One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions, arXiv:2607.10252v1.
- Author dataset and derived results, DOI 10.5281/zenodo.21278557, CC BY 4.0.
- Author collection and analysis software, DOI 10.5281/zenodo.21278793, MIT.
- VerifyLLMAPI atlas JSON, built from reference
pamela-openrouter-2026-07-8cell-ref-a, SHA-25651f3f63cbcfb56b151055d2d10e36a9cc2f0644cc68b580784853d68951fa866.
Page evidence SHA-256: 65bb4a97771f4051707a74eb08160c8fbcfd481257de4477368571fe67183be7. This hash covers the model ID, eight cell metrics, nearest references, and strongest cell difference in the generated atlas.