moonshotai/kimi-k2.6 behavioral LLM fingerprint
This page breaks down the exact reference that VerifyLLMAPI uses for moonshotai/kimi-k2.6: 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
- moonshotai/kimi-k2.5
- Nearest mean JSD
- 0.411548
The most concentrated cell was color-random|zh: 靛蓝 held 66.7% of its answers and entropy measured 1.559 bits. The broadest cell was letter-random|zh at 3.590 bits.
Against the closest enrolled model, letter-random|zh contributed the largest cell distance at JSD 0.775489. 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 · 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 · 14/15 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 platypus at 6/15 (40.0%); entropy was 2.289 bits.
| Normalized response | Count | Share |
|---|---|---|
platypus |
6 | 40.0% |
axolotl |
3 | 20.0% |
aardvark |
2 | 13.3% |
octopus |
2 | 13.3% |
elephant |
1 | 6.7% |
jaguar |
1 | 6.7% |
2. word-random|en
Random word, English. 15 valid reference answers produced 12 normalized values. The mode was serendipity at 3/15 (20.0%); entropy was 3.457 bits.
| Normalized response | Count | Share |
|---|---|---|
serendipity |
3 | 20.0% |
whisper |
2 | 13.3% |
banana |
1 | 6.7% |
hippopotamus |
1 | 6.7% |
indigo |
1 | 6.7% |
kaleidoscope |
1 | 6.7% |
lighthouse |
1 | 6.7% |
luminescent |
1 | 6.7% |
luminous |
1 | 6.7% |
quokka |
1 | 6.7% |
umbrella |
1 | 6.7% |
vortex |
1 | 6.7% |
3. animal-random|ru
Random animal, Russian. 15 valid reference answers produced 9 normalized values. The mode was капибара at 3/15 (20.0%); entropy was 3.006 bits.
| Normalized response | Count | Share |
|---|---|---|
капибара |
3 | 20.0% |
слон |
3 | 20.0% |
кот |
2 | 13.3% |
кошка |
2 | 13.3% |
бабочка |
1 | 6.7% |
красный |
1 | 6.7% |
лев |
1 | 6.7% |
медведь |
1 | 6.7% |
пингвин |
1 | 6.7% |
4. animal-random|ar
Random animal, Arabic. 15 valid reference answers produced 8 normalized values. The mode was فهد at 6/15 (40.0%); entropy was 2.606 bits.
| Normalized response | Count | Share |
|---|---|---|
فهد |
6 | 40.0% |
فيل |
2 | 13.3% |
نمر |
2 | 13.3% |
أسد |
1 | 6.7% |
الأسد |
1 | 6.7% |
زرافة |
1 | 6.7% |
قط |
1 | 6.7% |
قطط |
1 | 6.7% |
5. letter-random|zh
Random character, Chinese. 15 valid reference answers produced 13 normalized values. The mode was 鹤 at 3/15 (20.0%); entropy was 3.590 bits.
| Normalized response | Count | Share |
|---|---|---|
鹤 |
3 | 20.0% |
喆 |
1 | 6.7% |
墨 |
1 | 6.7% |
曦 |
1 | 6.7% |
梦 |
1 | 6.7% |
森 |
1 | 6.7% |
榫 |
1 | 6.7% |
橤 |
1 | 6.7% |
秋 |
1 | 6.7% |
雁 |
1 | 6.7% |
霡 |
1 | 6.7% |
风 |
1 | 6.7% |
龘 |
1 | 6.7% |
6. animal-random|zh
Random animal, Chinese. 15 valid reference answers produced 10 normalized values. The mode was 长颈鹿 at 4/15 (26.7%); entropy was 3.107 bits.
| Normalized response | Count | Share |
|---|---|---|
长颈鹿 |
4 | 26.7% |
猫 |
2 | 13.3% |
穿山甲 |
2 | 13.3% |
企鹅 |
1 | 6.7% |
大象 |
1 | 6.7% |
水獭 |
1 | 6.7% |
狐獴 |
1 | 6.7% |
狼 |
1 | 6.7% |
老虎 |
1 | 6.7% |
虎 |
1 | 6.7% |
7. letter-random|ar
Random letter, Arabic. 14 valid reference answers produced 7 normalized values. The mode was م at 8/14 (57.1%); entropy was 2.093 bits.
| Normalized response | Count | Share |
|---|---|---|
م |
8 | 57.1% |
ب |
1 | 7.1% |
ث |
1 | 7.1% |
د |
1 | 7.1% |
ذ |
1 | 7.1% |
ض |
1 | 7.1% |
ظ |
1 | 7.1% |
8. color-random|zh
Random color, Chinese. 15 valid reference answers produced 5 normalized values. The mode was 靛蓝 at 10/15 (66.7%); entropy was 1.559 bits.
| Normalized response | Count | Share |
|---|---|---|
靛蓝 |
10 | 66.7% |
紫色 |
2 | 13.3% |
青色 |
1 | 6.7% |
靛蓝紫 |
1 | 6.7% |
靛青 |
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 | moonshotai/kimi-k2.5 |
0.411548 |
| 2 | moonshotai/kimi-k2-0905 |
0.412964 |
| 3 | moonshotai/kimi-k2 |
0.440227 |
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: ffa36b39830e1f2282e11d71cd5814e3adcf880b7f2f75b616e0438bd20cf42b. This hash covers the model ID, eight cell metrics, nearest references, and strongest cell difference in the generated atlas.