xiaomi/mimo-v2.5-pro behavioral LLM fingerprint
This page breaks down the exact reference that VerifyLLMAPI uses for xiaomi/mimo-v2.5-pro: 77 normalized answers across eight low-entropy probes, plus its nearest enrolled models by mean Jensen–Shannon divergence.
Fingerprint summary
- Reference answers
- 77
- Samples per cell
- 6–12
- Nearest model
- z-ai/glm-5-turbo
- Nearest mean JSD
- 0.533681
The most concentrated cell was animal-random|ar: قط held 90.9% of its answers and entropy measured 0.439 bits. The broadest cell was word-random|en at 3.322 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 77/120 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 · 11/15 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 · 10/15 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 · 11/15 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 · 11/15 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 · 12/15 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 · 7/15 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 · 9/15 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 · 6/15 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. 11 valid reference answers produced 2 normalized values. The mode was elephant at 9/11 (81.8%); entropy was 0.684 bits.
| Normalized response | Count | Share |
|---|---|---|
elephant |
9 | 81.8% |
giraffe |
2 | 18.2% |
2. word-random|en
Random word, English. 10 valid reference answers produced 10 normalized values. The mode was azure at 1/10 (10.0%); entropy was 3.322 bits.
| Normalized response | Count | Share |
|---|---|---|
azure |
1 | 10.0% |
bubble |
1 | 10.0% |
ephemeral |
1 | 10.0% |
glimmer |
1 | 10.0% |
juxtaposition |
1 | 10.0% |
luminescence |
1 | 10.0% |
serendipity |
1 | 10.0% |
spark |
1 | 10.0% |
sunlight |
1 | 10.0% |
velvet |
1 | 10.0% |
3. animal-random|ru
Random animal, Russian. 11 valid reference answers produced 5 normalized values. The mode was лиса at 4/11 (36.4%); entropy was 2.118 bits.
| Normalized response | Count | Share |
|---|---|---|
лиса |
4 | 36.4% |
жираф |
3 | 27.3% |
коала |
2 | 18.2% |
волк |
1 | 9.1% |
слон |
1 | 9.1% |
4. animal-random|ar
Random animal, Arabic. 11 valid reference answers produced 2 normalized values. The mode was قط at 10/11 (90.9%); entropy was 0.439 bits.
| Normalized response | Count | Share |
|---|---|---|
قط |
10 | 90.9% |
ببغاء |
1 | 9.1% |
5. letter-random|zh
Random character, Chinese. 12 valid reference answers produced 4 normalized values. The mode was 龙 at 9/12 (75.0%); entropy was 1.208 bits.
| Normalized response | Count | Share |
|---|---|---|
龙 |
9 | 75.0% |
云 |
1 | 8.3% |
森 |
1 | 8.3% |
雪 |
1 | 8.3% |
6. animal-random|zh
Random animal, Chinese. 7 valid reference answers produced 4 normalized values. The mode was 狮子 at 2/7 (28.6%); entropy was 1.950 bits.
| Normalized response | Count | Share |
|---|---|---|
狮子 |
2 | 28.6% |
猫 |
2 | 28.6% |
袋鼠 |
2 | 28.6% |
长颈鹿 |
1 | 14.3% |
7. letter-random|ar
Random letter, Arabic. 9 valid reference answers produced 5 normalized values. The mode was م at 3/9 (33.3%); entropy was 2.197 bits.
| Normalized response | Count | Share |
|---|---|---|
م |
3 | 33.3% |
ب |
2 | 22.2% |
ر |
2 | 22.2% |
ع |
1 | 11.1% |
ق |
1 | 11.1% |
8. color-random|zh
Random color, Chinese. 6 valid reference answers produced 2 normalized values. The mode was 蓝色 at 4/6 (66.7%); entropy was 0.918 bits.
| Normalized response | Count | Share |
|---|---|---|
蓝色 |
4 | 66.7% |
蓝 |
2 | 33.3% |
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 | z-ai/glm-5-turbo |
0.533681 |
| 2 | x-ai/grok-4.20 |
0.538196 |
| 3 | z-ai/glm-4.6 |
0.546824 |
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: 01a2d747c7dcab96653182e4e1e67a8f79727e78b19c164b955a9e3c17bafa92. This hash covers the model ID, eight cell metrics, nearest references, and strongest cell difference in the generated atlas.