Open-source AI models are now embedded in academic tools, coding assistants and enterprise workflows. Most AI detectors were built for headline models only. GPTOne covers Meta Llama 4, Mistral Large, Qwen 3 and Microsoft Phi 4 so your detection extends to every model your users might be using.
Most AI detectors were built when ChatGPT was the only model widely in use. GPTOne was built for 2026, where Meta Llama, Mistral, Qwen and Phi are deployed across every industry and every continent.
Full coverage from Llama 2 through Llama 3, Llama 3.1 (405B), Llama 3.2, Llama 3.3 and Llama 4. Meta's open-source models are widely deployed in local tools, academic research platforms and business applications.
99.99% accuracyFull Mistral family coverage including Mistral 7B, Mixtral 8x7B, Mistral Large, Mistral Small, Mistral Nemo and Codestral. Widely used across European enterprise environments and coding tools.
99.99% accuracyCoverage for Alibaba Qwen 2, Qwen 2.5, QwQ and Qwen 3 reasoning models alongside Microsoft Phi-3 and Phi-4 series. Increasingly used in academic and business writing globally.
99.99% accuracyFalse positive rate below 5% holds across all open-source model families, including writing from non-native speakers and formal academic styles.
<5% false positivesNo account, no plugins, no waiting. GPTOne's multi-model classifier runs your text against open-source and proprietary AI model families simultaneously to return a complete picture of the AI origin.
Copy any essay, report, blog post, or code comment into GPTOne's free detector. No formatting required and plain text works best.
GPTOne's classifier checks for statistical patterns specific to open-source models including Meta Llama, Mistral, Qwen and Phi alongside ChatGPT, Claude and Gemini, all simultaneously rather than one family at a time.
You receive an overall AI probability score plus highlighted sections showing exactly which passages drove the score, so you know where to look and not just what the number says.
Use the result as a first-pass signal. For high-stakes decisions, combine with workflow guidance in our detection guides for educators, developers, publishers and compliance teams.
Most AI detectors do not report accuracy figures for open-source model families at all. The table below shows what each tool covers on the models most likely to appear in real-world submissions today.
| Detector | Llama accuracy | Mistral accuracy | False positive rate | Open-source coverage | Free to use |
|---|---|---|---|---|---|
| GPTOne Best | 99.99% | 99.99% | <5% | ✓ All major OSS | ✓ Full access |
| GPTZero | N/A | N/A | 6.8% | ✗ | Limited tier |
| Copyleaks | N/A | N/A | 5.1% | ✗ | Limited tier |
| ZeroGPT | N/A | N/A | 8.4% | ✗ | ✓ Full access |
N/A indicates that the tool does not publish accuracy figures for that model family. Competitor false positive rates are estimates based on GPTOne's internal comparative benchmark testing and independently reported user studies. GPTOne figures reflect internal benchmark data. No tool has been independently peer-reviewed.
Llama is running locally on student laptops. Mistral is deployed in European enterprise writing tools. Qwen is embedded in academic platforms across Asia. GPTOne gives every team the coverage that proprietary-only detectors cannot provide.
Llama is deployed locally in many university computing environments, allowing students to run AI writing tools entirely offline and off cloud services. GPTOne's section-level highlighting identifies Llama-generated passages that other detectors classify as human-written text.
Research integrityMistral and Codestral are widely used in code completion and documentation generation tools. Development teams reviewing code comments, pull request descriptions and technical documentation can use GPTOne to flag AI-generated content in their review workflow.
Code reviewContributor and freelance content increasingly originates from Qwen and Mistral models, especially from writers in Europe and Asia where these models have the strongest adoption. GPTOne's multi-family scanning gives editorial teams a complete picture rather than the ChatGPT-only slice that most tools provide.
Content QAAI use policies written when ChatGPT was the dominant model were not designed for the open-source deployment landscape of 2026. GPTOne ensures that integrity workflows cover Llama deployed locally, Mistral in European enterprise writing tools, and Phi embedded in Microsoft products.
Academic integrityGPTOne detects all major open-source AI model families. For Meta: Llama 2, Llama 3, Llama 3.1 (405B), Llama 3.2, Llama 3.3 and Llama 4. For Mistral AI: Mistral 7B, Mixtral 8x7B, Mistral Large, Mistral Small, Mistral Nemo and Codestral. For Alibaba: Qwen 2, Qwen 2.5, QwQ and Qwen 3 reasoning models. For Microsoft: Phi-3 Mini, Phi-3 Medium, Phi-3.5 and Phi-4. Coverage is updated as new versions are released by each provider.
Open-source models are harder to detect for several reasons. First, they can be fine-tuned by anyone, which shifts the statistical output patterns from the base model. Second, they are deployed across a wide range of environments including local tools, enterprise platforms and academic software, each of which may apply different system prompts and sampling configurations. Third, most AI detectors were benchmarked only against ChatGPT and GPT-4, so they have no training signal for Llama, Mistral or Qwen patterns. GPTOne trains on diverse outputs from each open-source model family to address these gaps directly.
GPTOne detects fine-tuned variants with meaningful accuracy by focusing on base model patterns that persist through fine-tuning rather than surface-level stylistic features that fine-tuning typically changes. The statistical fingerprints introduced during pre-training are durable across most fine-tuning approaches. Heavily instruction-tuned or RLHF-aligned derivatives may show reduced but still meaningful detection rates. For Llama-based derivatives and Mistral fine-tunes used in popular chatbot and writing assistant platforms, GPTOne's coverage extends to the underlying base model patterns.
Yes. GPTOne was trained specifically on Qwen 2, Qwen 2.5, QwQ and Qwen 3 outputs as well as Microsoft Phi-3 and Phi-4 series outputs. These models are increasingly used in academic writing tools and business applications globally, particularly in markets where Chinese and European AI providers are more prevalent than US providers. GPTOne's detection accuracy on Qwen and Phi families matches its overall 99.99% benchmark figure measured across all supported model families.
Yes, GPTOne is fully free to use for all open-source model detection including Meta Llama, Mistral, Qwen and Phi. No account is required and there is no word limit on individual scans. Paste your text at gptone.me and results appear within seconds. The free tier covers the complete multi-model detection scan across all supported model families without any gating behind a subscription.
GPTOne uses a multi-family training approach where each open-source model family is trained as a distinct classification target. This means the classifier learns separate feature sets for Llama outputs, Mistral outputs, Qwen outputs and Phi outputs rather than collapsing them into a single AI category. The result is that stylistic diversity across model families improves rather than degrades detection accuracy, because each family's distinct statistical patterns are modelled individually rather than averaged into a single representation.
Complete model coverage
Every major AI model family in one free tool. Follow the links below for model-specific detection guides.
The only free AI detector that covers all major open-source model families alongside ChatGPT, Claude and Gemini. No account. No cost. Results in seconds.
Run a free multi-model AI scanFree forever · No sign-up · Llama · Mistral · Qwen · Phi · And more