Reading the document types
- System card
- Describes a deployed system — model plus safeguards, evaluations, red-teaming results and known limitations at release. The frontier labs publish one per major release; it is the closest thing to a safety datasheet and the first document to pull for a risk assessment.
- Model card
- Describes the model artifact itself — intended use, training summary, benchmarks, license. Standard for open-weight releases, usually on the Hugging Face repository alongside the weights.
- Technical report
- Architecture, training method and evaluation detail. Depth varies enormously: some vendors publish data recipes, others a benchmark table.
For EU deployments these documents map directly onto obligations: the AI Act's general-purpose AI rules (applicable since August 2025) require providers to maintain technical documentation and training-data summaries, and downstream deployers inherit the need to understand them. See the Commission's AI Act framework.
Frontier labs — system cards per release
Proprietary vendors publish safety and system documentation per model release, at varying depth.
Anthropic 🇺🇸 USA
System cards for each Claude release, plus a transparency hub covering safety framework, evaluations and government commitments. Among the most detailed system cards published.
OpenAI 🇺🇸 USA
System cards per release (GPT-5 onward under the unified safety framework) plus preparedness framework reports and the safety hub.
Google DeepMind 🇺🇸 USA
Model pages with technical reports and model/system cards for the Gemini family, plus the Frontier Safety Framework documentation.
Microsoft AI 🇺🇸 USA
Model pages for the MAI family; the MAI-Thinking-1 technical report also covers the lineage behind MAI-Cyber-1-Flash.
Meta 🇺🇸 USA
Developer documentation for the Muse family and legacy Llama model cards. Note the documentation split after the shift from open-weight Llama to proprietary Muse.
xAI 🇺🇸 USA
Model announcements and documentation for the Grok family; safety documentation is comparatively thin — worth noting in assessments.
Thinking Machines Lab 🇺🇸 USA
The Inkling announcement doubles as a technical report — architecture, training data scale, safety benchmarks (FORTRESS) and calibration methodology.
Open-weight vendors — model cards with the weights
For open models the primary documentation lives on the Hugging Face organisation pages, next to the checkpoints.
DeepSeek 🇨🇳 China
Model cards and technical reports for the V4 family and predecessors, published with MIT-licensed weights.
Alibaba Qwen 🇨🇳 China
Cards for the Qwen 3.x open-weight line, including the 3.6-generation MoE models.
Z.ai (Zhipu) 🇨🇳 China
GLM-5.x model cards and agentic evaluation detail, MIT-licensed.
Moonshot AI 🇨🇳 China
Kimi model cards; check here for the K3 open-weight checkpoint status.
Xiaomi MiMo 🇨🇳 China
MiMo-V2.5-Pro card with training methodology and agentic benchmark detail.
StepFun 🇨🇳 China
Step 3.7 Flash cards including quantised deployment recipes for local use.
NVIDIA 🇺🇸 USA
Nemotron 3 model cards — unusual for including the training datasets themselves, not just their description.
Mistral AI 🇫🇷 France
Model cards and API documentation for the open and commercial lines — the EU-jurisdiction reference vendor.
IBM Granite 🇺🇸 USA
Granite 4 cards with the governance extras: signed weights and ISO/IEC 42001-accredited process documentation.
Cisco Foundation AI 🇺🇸 USA
Cards for the security models — Foundation-Sec-8B and the Antares family (gated access).
Model-card standard 🌐
Hugging Face's documentation of the model-card format itself — useful when writing internal cards for fine-tuned models.
Independent evaluation & tracking
Vendor documentation states claims; these sources test or track them independently — always pair the two.
UK AI Security Institute 🇬🇧 UK
Government pre-deployment evaluations of frontier models, including the cyber-range exercises referenced for Mythos-class models.
Artificial Analysis 🌐
Independent intelligence, speed and price benchmarking across hosted models — the fastest way to sanity-check a vendor benchmark table.
Stanford HELM 🇺🇸 USA
Academic holistic evaluation framework with transparent, reproducible scenario coverage.
LMArena 🌐
Blind human-preference rankings — noisy but hard to game in the ways static benchmarks are.
Epoch AI 🌐
Data and analysis on compute, parameters and training trends — the source for scale context behind the registry's log axis.
models.dev & OpenRouter 🌐
Machine-readable model specification and pricing databases — practical sync sources for keeping the registry's pricing fields current.