- Briefing
- Stage Test · 1/3
Test
The fingerprints a model leaves
Nine tests. The hardest: +75, L1=0 and video tokens identical to GLM-5V-Turbo.
- You will understand
- See the nine tests one by one.
- Understand why +75 is hard evidence.
- Do not confuse a fingerprint with a passport.
A · tokenizer
Tokenizer test (dictionary fingerprint)
Ox Alpha = GLM-5.3 + 75 on EVERY text
If two models cut the same text into the same number of pieces, they use the same dictionary. A fixed +75 is the “envelope” (system prompt) OpenRouter adds. If the dictionary were different, the extra would NOT be constant.
Δ(P) = tok_ox(P) − tok_glm(P) = 75 ∀ P ∈ {EN, DE, CN, código, emoji}15-year-old version. Two people cut a newspaper with the same scissors. One always gets 75 extra clippings, because they add a letterhead. The scissors are the same.
A · l1
Tokenizer L1 vector test
L1=0 vs GLM-5.3; MiMo 176; MiniMax 172; others 148–302
The same rare texts go to two APIs and the counts are subtracted. L1=0 = same knife. 150–300 = a random knife. Tony Zhou published Ox vector [76, 384, 114, 64, 49, 313] identical to z-ai/glm-5.3.
L1(v_ox, v_glm) = Σ |v_ox,i − v_glm,i| = 0 · v = [76, 384, 114, 64, 49, 313] · L1(ox, MiMo)=176 · L1(ox, MiniMax)=172
15-year-old version. Two keys: if they open the same 6 locks, they are copies. If one misses the emoji by 33 tokens, it is not the copy.
A− · error
Error-code test
Errors 1210 and 1214 identical to chat.z.ai
When an API fails, the server dumps an internal number. That number is a factory stamp. 1210 had already been seen on Z.ai.
P(código 1210 | backend z.ai) ≫ P(código 1210 | backend ajeno)
15-year-old version. If the ATM says “error 12-B” and only the bank on the corner uses that code, you know whose ATM it is even without a logo.
A− · video
Video-encoder test
Token budget per frame = GLM-5V-Turbo
Putting video into a text model means turning frames into tokens. Each lab chooses how many tokens “a second of video costs”. Ox Alpha charges the same as GLM-5V-Turbo.
tokens_video(T, fps) ≈ k_GLM5V · frames(T, fps)
15-year-old version. Like a toll: if every car pays exactly 8 chips and only one booth charges 8, it is not the highway next door (which charges 5 or 12).
B · cache
Prefix-cache test
One tester (pi.dev) measured ~97%; OpenRouter lists ~87% Ox vs ~92% GLM-5.3
If you repeat the repo, the server reuses compute. 97% on an agent session is a kitchen signature, not marketing. The OpenRouter catalog says 87%: useful, not unique. Do not treat it as a passport.
hit_rate(sesión pi.dev)≈0.97 · hit_rate(catálogo OR)≈0.87 ≠ prueba de identidad sola
15-year-old version. Your regular bar remembers the order (97%). TripAdvisor ranks it 87%. Both numbers can be true.
B · jailbreak
Identity-leak test
“I'm GLM, a large language model developed by Z.ai”
Models have a system prompt that says who they are. Sometimes a jailbreak makes them vomit it. Behavioral evidence, easier to fake than a tokenizer.
evidencia blanda: P(fuga GLM | es GLM) alta, pero P(fuga | wrapper) también > 0
15-year-old version. If you pull someone’s carnival mask off and they yell their real name, that is a clue. They could also be acting.
B · audio
Audio-rejection test
Accepts image and video; rejects audio — like GLM vision
Inkling (Mira Murati) accepts audio. MiMo-Omni too. Classic GLM vision does not. Ox Alpha aligns with GLM, not Inkling.
modalidad(Ox) = {text, image, video} = modalidad(GLM-V) ≠ {text, image, audio}15-year-old version. It does not eat peanuts. The other cousin does. It is not that cousin.
B− · dirty
Dirty-token test
Trips on the same rare token as GLM / Qwen / MiMo
Some Chinese tokenizers share quirks. Confirms “Chinese lab”, not which one.
pertenencia a familia tokenizer CJK, no identificador único
15-year-old version. The accent says “this country”. It does not say the city.
A− · logs
Chinese log / ModelScope test
Invalid params → Chinese error logs + GLM ModelScope strings
On 21 Aug @JoshRadDev sent bad parameters and got Chinese backend logs plus strings that match other GLM errors on ModelScope. Hard to fake from a US stack.
P(log CJK + ModelScope GLM | backend z.ai) ≫ P(mismo log | Cursor/OpenAI)
15-year-old version. The mechanic dropped a Chinese-language manual. The garage is not in California.
Lab: cut the text
This is not the real GLM vocab. It is a simulator: two knives, two counts. In the real test, Ox and GLM-5.3 match; Kimi/MiMo/Qwen do not.
Why
Compact style (CJK/GLM family, illustrative) · 11 pieces
Western, more split (illustrative) · 14 pieces
If Ox Alpha uses the GLM knife, its count is 86 = 11 pieces + 75 letterhead. The Western knife gives 14.
Rebuilt table · +75 test
Published pattern (dax / r/singularity / MaxForAI / Davis): every text is exactly 75 tokens more on Ox than on GLM-5.3.
| Prompt | GLM | Ox | Δ | MiMo | Kimi |
|---|---|---|---|---|---|
| EN Hello, how are you today? | 8 | 83 | 75 | 10 | 9 |
| DE Guten Morgen, wie geht es di | 11 | 86 | 75 | 13 | 14 |
| CN 你好，今天过得怎么样？ | 9 | 84 | 75 | 11 | 8 |
| code def fib(n): return n if n<2 | 24 | 99 | 75 | 26 | 28 |
| emoji 🚀🔥💡✨🎯 | 10 | 85 | 75 | 14 | 15 |
| EN The quick brown fox jumps ov | 12 | 87 | 75 | 14 | 13 |
Illustrative numbers of the public pattern, not our own run. The constant is the finding.
Published runs
| Tester | N | Δ | Note |
|---|---|---|---|
| r/singularity | 6 | 75 | EN/DE/CN/código/emoji. Kimi/Qwen/MiMo/MiniMax divergen. Ambas rutas Ox idénticas. |
| @aitrackerbot | 25 | 75 | Primera tabla pública. MiniMax M3 no matcheó. |
| @RocketmanSh | 4 | 75 | Pares crudos 88/106/97/108 vs 13/31/22/33. También error 1210/1214. |
| @unclecode / modelprint | 4 | same-vocab | GLM tokenizer 4/4. Kimi 0/4, MiniMax 0/4, Qwen 1/4, MiMo ≤2/4 (emoji 90 vs 57). |
| @pritish_yuvi | 17 | same-vocab | Multilingüe/código/emoji exactamente como GLM-5. «MiMo didn’t match.» |
| @BohuTANG | 4 | 75 | EN/CN/código/random. «MiMo 完全不同». |
| @KatilaMarriete | 7 | 75 | OpenRouter Ox ≡ OpenCode Zen; ambos +75 vs GLM-5.3. El 75 es wrapper de ruta. |
| @evverin | 6 | 75 | Seis tests multilingual. Offset exacto 75. |
| @sushsrinivasan | 11 | same-vocab | Ox = GLM 11/11. Ningún otro lab pasa de 4/11. DeepSeek dígitos 98 vs GLM 29. |
| @filicroval | 12 | same-vocab | Mismos números. Tokenizer compartido. |
| @TonyJZhou | 6 | same-vocab | Vector [76, 384, 114, 64, 49, 313] = glm-5.3. L1=0. MiMo 176. MiniMax 172. |
| @MaxForAI | 25 | 75 | Recap chino del test de aitrackerbot. No es una cuenta original. |
| @thdxr (dax) | 0 | 75 | Sin tabla. No independiente. Officechai le atribuyó por error el N=25. |
RocketmanSh raw pairs: always 75 extra. Independent = original table, not a recap.
- 88 − 13 = 75
- 106 − 31 = 75
- 97 − 22 = 75
- 108 − 33 = 75