Public AI

A wide range of public‑access large language models can support CE‑marking work such as scoping legislation, drafting risk assessments, mapping harmonised standards, and preparing technical documentation. The models below are suitable because they provide general‑purpose reasoning, structured output, and multilingual capability—while leaving legal responsibility with the manufacturer.

Major public‑access LLMs suitable for CE‑marking tasks

Microsoft Copilot (GPT‑4/4.1 family)

Website: https://copilot.microsoft.com
Key strengths:

• Strong reasoning and structured‑output capabilities.

• Integrated with Microsoft 365, making it useful for drafting technical files, DoCs, and checklists.

• Good at long‑context tasks (e.g., reviewing risk assessments).
  Key differences:

• More conservative and safety‑aligned than many models.

• Strong at self‑checking and step‑by‑step reasoning when prompted correctly.

OpenAI ChatGPT (GPT‑4.1 / GPT‑4o / GPT‑3.5)

Website: https://chat.openai.com
Key strengths:

• Excellent chain‑of‑thought reasoning and structured analysis.

• Very strong at generating templates (risk assessments, DoCs, technical‑file structures).

• Good multilingual support for EU documentation.
  Key differences:

• GPT‑4‑class models outperform GPT‑3.5 significantly for regulatory tasks.

• Paid versions offer better accuracy and longer context windows.

Google Gemini (Gemini Pro / Gemini Ultra)

Website: https://gemini.google.com
Key strengths:

• Strong at summarising long legal texts and extracting essential requirements.

• Good at cross‑document consistency checking (e.g., comparing risk assessment vs. DoC).

• Excellent multilingual capabilities.
  Key differences:

• More concise by default; benefits from explicit chain‑of‑thought prompting.

• Very strong at factual grounding when asked to cite sources.

Anthropic Claude (Claude 3 Opus / Sonnet / Haiku)

Website: https://claude.ai
Key strengths:

• Exceptional at long‑context analysis (up to hundreds of pages).

• Very good at legal‑style reasoning and structured compliance outputs.

• Strong hallucination‑avoidance when using self‑checking prompts.
  Key differences:

• Opus is one of the best models for deep reasoning; Haiku is fast and lightweight.

• Often produces the most cautious and well‑structured compliance text.

Meta Llama‑3 (via HuggingFace, Perplexity, Poe, etc.)

Website: https://huggingface.co/meta-llama
Key strengths:

• Open‑weights model—can be run locally for sensitive documentation.

• Good for drafting templates and performing structured classification tasks.
  Key differences:

• Weaker than GPT‑4/Gemini/Claude for complex legal reasoning.

• Requires stronger prompting to avoid hallucinations.

Mistral (Mistral Large / Mixtral)

Website: https://mistral.ai
Key strengths:

• Strong open‑weights options for on‑premise or private environments.

• Good at structured outputs and multilingual tasks.
  Key differences:

• Slightly less reliable for deep regulatory interpretation unless guided with atom‑of‑thought prompting.

Why these models are suitable for CE‑marking work

• They can analyse legislation, including Regulation (EU) 2023/1230.

• They can generate structured outputs (risk tables, DoCs, checklists).

• They support multilingual EU documentation.

• They can perform self‑checking when prompted (reducing hallucinations).

• They can handle long technical files and cross‑document consistency checks.

A natural next step is identifying which of these models best fits your workflow—cloud‑based, hybrid, or on‑premise—given your focus on CE/EC compliance automation.

How these LLMs differ in CE‑marking use cases (summary)