I added Fable5 to the test and found that it does not achieve extremely high (>0.9) similarity with most of Anthropic’s models.

This likely indicates that it is a new base model trained on an unprecedented mixture of data (this is just speculation; post-training also affects the fingerprint results, so take it as a reference).

But an additional surprise is that kimi k2.7, qwen3-7max, and glm5.2 all show extremely high similarity with Fable?? (0.97/0.93)

Could it have leaked and been distilled back during mythos? (This is just speculation; please view it rationally.)

And it shows very low similarity with Opus 4.8, which probably indicates that Opus 4.7/4.8 are indeed not on the same branch as Fable/Sonnet 5, confirming why we have been complaining about Opus 4.7/4.8.

Project at: https://github.com/qqqqqf-q/ai-model-fingerprint…

Using OpenRouter API at standard pricing.

qqqqqf-q/ai-model-fingerprint

Source: https://github.com/qqqqqf-q/ai-model-fingerprint

AI Model Random Number Fingerprint Dataset

Using the statistical distribution fingerprint of “random number selection” to distinguish/detect 17 AI models. The principle originates from hlwy-ai-checker (https://github.com/hanlinwenyuan/hlwy-ai-checker).

Principle

Large models are not true random number generators. With a fixed prompt “Please randomly select a number from 1 to 355” and temperature=1.0, intensive sampling reveals that different models, due to differences in training data/architecture/RLHF/tokenizer, produce statistically distinguishable distribution fingerprints. This fingerprint is not easily overridden by system prompts and can be used to detect whether third-party APIs are adulterated.

Collection

• OpenRouter API, 17 models, ~300 valid samples per model (minimax post-processing recovered 622)
• Fixed prompt, max_tokens=32, most models use reasoning.enabled=false to disable reasoning
• grok-build-0.1 / kimi-k2.7-code force reasoning, switch to max_tokens=2048
• Each request fully saved: raw response body / system_fingerprint / usage (including cost) / request parameters / latency / http_status
• Supports resume from breakpoint, concurrency, 429 backoff retry

Data Structure

Models (17)

claude opus 4.6/4.7/4.8, sonnet 4.6, haiku 4.5; gpt-5.5/5.4/4o-mini; glm-5.2; deepseek v3.2/v4-flash/v4-pro; minimax-m3; qwen3.7-plus; grok-build-0.1; kimi k2.6/k2.7-code

Key Findings

• claude opus 4.6 ≈ 4.7 (cos=0.997, fingerprints almost identical), but 4.8 is completely different (cos≈0.04), and 4.8 unique=2 quasi-fixed output (300 times almost only outputs 237)
• kimi k2.6 vs k2.7-code (same base, k2.7 is post-training version): cos=0.631, post-training significantly changes the fingerprint
• deepseek same stack different corpora v3.2 / v4-flash / v4-pro mutual cos=0.15~0.31
• Cross-model most similar: haiku-4.5 ≈ sonnet-4.6 (0.997), kimi-k2.7-code ≈ glm-5.2 (0.916)

Reproduction

bash pip install -r requirements.txt cp config.example.yaml config.yaml # Fill in OpenRouter key python run.py --samples 300 # Collection python check_data.py # Integrity check python analyze.py # Statistics + charts python estimate.py # Token/cost estimation

Cost

Full collection costs approximately $1.03 (grok reasoning accounts for the majority, 637 output tokens/req).