LLM Detector — Any Large Language Model
ShaamAI detects text from any major LLM — GPT, Claude, Gemini, Grok, DeepSeek, Llama, and more. Model-agnostic detection with sentence-level detail.
How Model-Agnostic Detection Works
Different LLMs sound different on the surface, but underneath they share statistical properties that human writing does not. Our detector measures those shared properties.
Multi-Signal Detection
We combine multiple stylistic features — burstiness (variance in sentence length), perplexity, entropy in word choice, and rhythmic consistency — into a single calibrated score. No single signal is definitive; combining them is what makes detection robust across LLMs.
Diverse Training Corpus
Our detector is trained on text spanning the major LLM families — GPT, Claude, Gemini, Grok, DeepSeek, Llama — as well as diverse human writing across genres. That diversity is what lets a single model generalize across the whole LLM landscape.
Zero-Shot Generalization
Detection does not require that we have seen the exact model in training. Because the underlying statistical signals are shared across LLMs, our detector often works on models released after its training. Confidence can be lower on very novel model families, and we report calibrated probabilities rather than absolute verdicts.
Sentence-Level Detail
Rather than a single black-box verdict, we surface the per-sentence signals driving the overall score. Edge cases — partly human, partly AI, or heavily edited text — become auditable: you can see exactly where the model sees strong AI signal and where it sees natural writing.
Model-Specific Detector Pages
Looking for a specific model? Each detector page covers how we identify that model's particular stylistic and structural fingerprint.
LLM Detection FAQ
What LLMs can ShaamAI detect?
ShaamAI is designed to detect output from every major large language model family — OpenAI's GPT (including GPT-4 and GPT-4o), Anthropic's Claude, Google's Gemini, xAI's Grok, DeepSeek's V3 and R1, Meta's Llama 3.x, Mistral, Qwen, and most of their downstream fine-tunes. Our detection is model-agnostic: it looks for the statistical fingerprints that all LLMs leave in text, rather than memorizing the style of any single model.
Can ShaamAI detect new LLMs released after its training?
Usually yes. Detection depends on shared structural properties of LLM output — low perplexity variance, narrow vocabulary distribution, uniform sentence rhythm, predictable transitions — not on memorizing any specific model. New models that share these properties tend to be detected well in zero-shot settings. Accuracy can be lower on models that differ substantially from any we have seen, but the general approach generalizes.
Which LLM is hardest to detect?
Models tuned to produce shorter, more varied output — or heavily sampled with high temperature and custom system prompts — are generally harder than default chat output. Short outputs under 50 words are also inherently harder for any detector because there is simply less signal to measure. We do not claim any detector is 100% accurate on any model, and our probability score reflects that — borderline scores mean borderline evidence.
Does ShaamAI detect AI-generated text from enterprise LLM deployments?
Yes. Enterprise deployments (on Azure, AWS Bedrock, Vertex AI, Together, Groq, Fireworks, Anthropic's API, and so on) serve the same underlying models behind a different API. The hosting layer does not change the text the model generates, so detection signals are preserved. Custom system prompts and RAG can add variation but do not eliminate the underlying LLM fingerprint.
How does model-agnostic detection work?
Instead of training a classifier that tries to recognize one specific model, we measure properties that are common to LLM output across families: per-token perplexity, burstiness (variance in sentence length and complexity), entropy in word choice, and rhythmic consistency across paragraphs. Human writing varies on all of these; LLM output tends to cluster narrowly. Combining multiple signals with a learned model gives us a calibrated probability score that works across LLMs we have seen and generalizes to new ones.
Does ShaamAI store my text?
Your text is transmitted over HTTPS to our servers for the duration of each analysis request. We do not save your text to any database, we do not use it to train or fine-tune our models, and we do not share it with third parties. Only anonymous metadata (word count, score, confidence) is retained for analytics.
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Paste any text — regardless of which LLM generated it — and get an instant AI probability score with sentence-level detail.
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