mistralai/ministral-8b-2512 behavioral LLM fingerprint
This page breaks down the exact reference that VerifyLLMAPI uses for mistralai/ministral-8b-2512: 119 normalized answers across eight low-entropy probes, plus its nearest enrolled models by mean Jensen–Shannon divergence.
Fingerprint summary
- Reference answers
- 119
- Samples per cell
- 14–15
- Nearest model
- mistralai/mistral-large-2512
- Nearest mean JSD
- 0.733760
The most concentrated cell was word-random|en: serendipity held 100.0% of its answers and entropy measured 0.000 bits. The broadest cell was letter-random|zh at 3.907 bits.
Against the closest enrolled model, color-random|zh 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 119/120 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 · 14/15 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 · 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 10 normalized values. The mode was koala at 5/15 (33.3%); entropy was 3.000 bits.
| Normalized response | Count | Share |
|---|---|---|
koala |
5 | 33.3% |
lemur |
2 | 13.3% |
dingo |
1 | 6.7% |
fox |
1 | 6.7% |
gorilla |
1 | 6.7% |
jaguar |
1 | 6.7% |
ostrich |
1 | 6.7% |
pangolin |
1 | 6.7% |
tarsier |
1 | 6.7% |
whale |
1 | 6.7% |
2. word-random|en
Random word, English. 15 valid reference answers produced 1 normalized values. The mode was serendipity at 15/15 (100.0%); entropy was 0.000 bits.
| Normalized response | Count | Share |
|---|---|---|
serendipity |
15 | 100.0% |
3. animal-random|ru
Random animal, Russian. 15 valid reference answers produced 11 normalized values. The mode was пингвин at 3/15 (20.0%); entropy was 3.273 bits.
| Normalized response | Count | Share |
|---|---|---|
пингвин |
3 | 20.0% |
слон |
3 | 20.0% |
волк |
1 | 6.7% |
корова |
1 | 6.7% |
кุงга |
1 | 6.7% |
лемур |
1 | 6.7% |
ленивец |
1 | 6.7% |
панда |
1 | 6.7% |
слонь |
1 | 6.7% |
тигр |
1 | 6.7% |
ягуар |
1 | 6.7% |
4. animal-random|ar
Random animal, Arabic. 14 valid reference answers produced 9 normalized values. The mode was دلفين at 4/14 (28.6%); entropy was 2.950 bits.
| Normalized response | Count | Share |
|---|---|---|
دلفين |
4 | 28.6% |
فيل |
2 | 14.3% |
قرد |
2 | 14.3% |
ثعلب |
1 | 7.1% |
دتيل |
1 | 7.1% |
دولفين |
1 | 7.1% |
سلحفاة |
1 | 7.1% |
قندس |
1 | 7.1% |
نمر |
1 | 7.1% |
5. letter-random|zh
Random character, Chinese. 15 valid reference answers produced 15 normalized values. The mode was 抽 at 1/15 (6.7%); entropy was 3.907 bits.
| Normalized response | Count | Share |
|---|---|---|
抽 |
1 | 6.7% |
旺 |
1 | 6.7% |
棘 |
1 | 6.7% |
森 |
1 | 6.7% |
烁 |
1 | 6.7% |
犀 |
1 | 6.7% |
芒 |
1 | 6.7% |
菁 |
1 | 6.7% |
菟 |
1 | 6.7% |
萤 |
1 | 6.7% |
蝉 |
1 | 6.7% |
诞 |
1 | 6.7% |
轩 |
1 | 6.7% |
逸 |
1 | 6.7% |
隽 |
1 | 6.7% |
6. animal-random|zh
Random animal, Chinese. 15 valid reference answers produced 9 normalized values. The mode was 狐狸 at 3/15 (20.0%); entropy was 2.956 bits.
| Normalized response | Count | Share |
|---|---|---|
狐狸 |
3 | 20.0% |
狒狒 |
3 | 20.0% |
狮子 |
3 | 20.0% |
刺猬 |
1 | 6.7% |
华南虎 |
1 | 6.7% |
大象 |
1 | 6.7% |
沙丁鱼 |
1 | 6.7% |
犀牛 |
1 | 6.7% |
狼 |
1 | 6.7% |
7. letter-random|ar
Random letter, Arabic. 15 valid reference answers produced 7 normalized values. The mode was ق at 5/15 (33.3%); entropy was 2.466 bits.
| Normalized response | Count | Share |
|---|---|---|
ق |
5 | 33.3% |
ف |
4 | 26.7% |
ج |
2 | 13.3% |
ب |
1 | 6.7% |
ح |
1 | 6.7% |
س |
1 | 6.7% |
ط |
1 | 6.7% |
8. color-random|zh
Random color, Chinese. 15 valid reference answers produced 7 normalized values. The mode was 天蓝 at 7/15 (46.7%); entropy was 2.330 bits.
| Normalized response | Count | Share |
|---|---|---|
天蓝 |
7 | 46.7% |
深藕紫 |
2 | 13.3% |
紫罗兰 |
2 | 13.3% |
水晶灰 |
1 | 6.7% |
淡紫罗兰 |
1 | 6.7% |
深蓝色 |
1 | 6.7% |
翡翠绿 |
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 | mistralai/mistral-large-2512 |
0.733760 |
| 2 | openai/gpt-5.1 |
0.734144 |
| 3 | mistralai/ministral-14b-2512 |
0.746042 |
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: 466eb2fc8a74d67f32fff83b6c0304a6e71cfe01aa19d426098d8378c8111da5. This hash covers the model ID, eight cell metrics, nearest references, and strongest cell difference in the generated atlas.