`meta-llama/llama-3.2-11b-vision-instruct` behavioral LLM fingerprint

This page breaks down the exact reference that VerifyLLMAPI uses for meta-llama/llama-3.2-11b-vision-instruct: 120 normalized answers across eight low-entropy probes, plus its nearest enrolled models by mean Jensen–Shannon divergence.

Fingerprint summary

Reference answers: 120
Samples per cell: 15–15
Nearest model: meta-llama/llama-3.1-8b-instruct
Nearest mean JSD: 0.636061

The most concentrated cell was word-random|en: computer held 33.3% of its answers and entropy measured 2.600 bits. The broadest cell was animal-random|ar at 3.907 bits.

Against the closest enrolled model, animal-random|ar contributed the largest cell distance at JSD 1.000000. 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 120/120 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 constraint Answer 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 constraint Answer 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 constraint Отвечай 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 constraint أجب 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 constraint أجب بكلمة واحدة أو رقم واحد فقط. لا تكتب أي شيء آخر: لا شرح ولا علامات ترقيم ولا علامات اقتباس.	grapheme · 15/15 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 9 normalized values. The mode was koala at 5/15 (33.3%); entropy was 2.816 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for animal-random|en
Normalized response	Count	Share
`koala`	5	33.3%
`kangaroo`	3	20.0%
`aardvark`	1	6.7%
`flamingo`	1	6.7%
`giraffe`	1	6.7%
`leopard`	1	6.7%
`ostrich`	1	6.7%
`pangolin`	1	6.7%
`penguin`	1	6.7%

2. word-random|en

Random word, English. 15 valid reference answers produced 8 normalized values. The mode was computer at 5/15 (33.3%); entropy was 2.600 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for word-random|en
Normalized response	Count	Share
`computer`	5	33.3%
`cloud`	4	26.7%
`banana`	1	6.7%
`candy`	1	6.7%
`chair`	1	6.7%
`elephant`	1	6.7%
`paprika`	1	6.7%
`pineapple`	1	6.7%

3. animal-random|ru

Random animal, Russian. 15 valid reference answers produced 12 normalized values. The mode was кит at 3/15 (20.0%); entropy was 3.457 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for animal-random|ru
Normalized response	Count	Share
`кит`	3	20.0%
`крокодил`	2	13.3%
`cеболь`	1	6.7%
`зебра`	1	6.7%
`кролик`	1	6.7%
`ланион`	1	6.7%
`лев`	1	6.7%
`морской`	1	6.7%
`носорог`	1	6.7%
`осьлер`	1	6.7%
`собака`	1	6.7%
`хорька`	1	6.7%

4. animal-random|ar

Random animal, Arabic. 15 valid reference answers produced 15 normalized values. The mode was createstacknavigator at 1/15 (6.7%); entropy was 3.907 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for animal-random|ar
Normalized response	Count	Share
`createstacknavigator`	1	6.7%
`pointingnder`	1	6.7%
`получения`	1	6.7%
`اتن`	1	6.7%
`الذئب`	1	6.7%
`الضفدع`	1	6.7%
`الظباء`	1	6.7%
`الغزال`	1	6.7%
`الفهود`	1	6.7%
`الكلاب`	1	6.7%
`النحسي`	1	6.7%
`اليربوع`	1	6.7%
`سهم`	1	6.7%
`فرس`	1	6.7%
`فهد`	1	6.7%

5. letter-random|zh

Random character, Chinese. 15 valid reference answers produced 15 normalized values. The mode was 233 at 1/15 (6.7%); entropy was 3.907 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for letter-random|zh
Normalized response	Count	Share
`233`	1	6.7%
`bí`	1	6.7%
`凹`	1	6.7%
`华`	1	6.7%
`庚`	1	6.7%
`播`	1	6.7%
`映`	1	6.7%
`梁`	1	6.7%
`椰`	1	6.7%
`油菜`	1	6.7%
`烟`	1	6.7%
`蓬`	1	6.7%
`蛋`	1	6.7%
`钉`	1	6.7%
`镜`	1	6.7%

6. animal-random|zh

Random animal, Chinese. 15 valid reference answers produced 14 normalized values. The mode was 松鼠 at 2/15 (13.3%); entropy was 3.774 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for animal-random|zh
Normalized response	Count	Share
`松鼠`	2	13.3%
`大象`	1	6.7%
`河狸`	1	6.7%
`河马`	1	6.7%
`海狮`	1	6.7%
`海狸bear`	1	6.7%
`狮子`	1	6.7%
`羊`	1	6.7%
`虎`	1	6.7%
`螃蟹`	1	6.7%
`貘`	1	6.7%
`青蛙`	1	6.7%
`鳄鱼`	1	6.7%
`黄雀`	1	6.7%

7. letter-random|ar

Random letter, Arabic. 15 valid reference answers produced 8 normalized values. The mode was م at 4/15 (26.7%); entropy was 2.740 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for letter-random|ar
Normalized response	Count	Share
`م`	4	26.7%
`ج`	3	20.0%
`ق`	3	20.0%
`ح`	1	6.7%
`ص`	1	6.7%
`ض`	1	6.7%
`ك`	1	6.7%
`ل`	1	6.7%

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 2.606 bits.

meta-llama/llama-3.2-11b-vision-instruct response distribution for color-random|zh
Normalized response	Count	Share
`紫色`	6	40.0%
`橙色`	2	13.3%
`蓝色`	2	13.3%
`皇色`	1	6.7%
`粉色`	1	6.7%
`绿色`	1	6.7%
`苹果红`	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-3.1-8b-instruct`	0.636061
2	`meta-llama/llama-3.3-70b-instruct`	0.667985
3	`meta-llama/llama-3.1-70b-instruct`	0.728754

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-256 51f3f63cbcfb56b151055d2d10e36a9cc2f0644cc68b580784853d68951fa866.

Page evidence SHA-256: b11cdcd9273a06dea75d3c69195dd2debf66056d02f024319bcbde3d71d89049. This hash covers the model ID, eight cell metrics, nearest references, and strongest cell difference in the generated atlas.

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