Anthropic identifies Claude 4 Sonnet as a 'hybrid reasoning' model built on a dense transformer foundation. While it documents the dual-state processing (standard vs. extended thinking) and the use of rotary positional encodings (RoPE) to support its context window, it provides no specific details on the underlying layer count, attention head configuration, or the specific modifications made to the transformer architecture. The 'hybrid' nature is described primarily as a functional capability rather than a detailed architectural specification.

Information regarding the training data is limited to high-level categories. Anthropic's system card states the model was trained on a 'proprietary mix' of publicly available internet data (as of March 2025), non-public third-party data, and data from opted-in users and contractors. No specific breakdown of dataset proportions (e.g., code vs. web vs. academic) is provided, and the exact filtering or cleaning methodologies remain undisclosed beyond general alignment goals.

The tokenizer is accessible via the Anthropic API and integrated into developer tools like Claude Code, allowing for empirical verification of token counts. However, official documentation lacks a detailed technical breakdown of the vocabulary size or the specific training data alignment for the Claude 4 generation's tokenizer compared to its predecessors.

Anthropic maintains a strict policy of not disclosing parameter counts for its proprietary models. While the model is described as 'dense,' there is no verifiable information regarding total or active parameters. Third-party estimates exist for previous versions, but no official or verifiable data is available for the Claude 4 family.

No specific information regarding GPU/TPU hours, hardware clusters, or total compute expenditure has been released. While Anthropic mentions environmental considerations in general terms, it does not provide a calculated carbon footprint or energy consumption report for the training of Claude 4 Sonnet.

Anthropic provides detailed benchmark results (e.g., 72.7% on SWE-bench Verified) and includes an appendix in its launch documentation describing the methodology (e.g., nucleus sampling, top_p of 0.95, and tool use). However, the full evaluation code and the exact prompts used for all academic benchmarks are not publicly released, limiting independent reproduction to third-party 'black-box' testing.

The model demonstrates high identity consistency, correctly identifying itself as Claude and specifying its version. It is transparent about its 'extended thinking' state and the limitations of its knowledge cutoff (March 2025). There are no documented instances of the model claiming to be a competitor's product.

The model is governed by a clear but restrictive proprietary license. Commercial use is permitted through the API and Enterprise plans, with explicit terms regarding output ownership. However, the 'consumer' terms for free users are more ambiguous regarding commercial rights, and the license for the weights themselves is non-existent as they are not public.

As a closed-source API-based model, local hardware requirements for weights are irrelevant. However, Anthropic provides some transparency regarding context-length memory scaling, noting that prompts over 200k tokens incur higher costs and latency. Documentation for developers using the API provides clear guidance on max output tokens (64k) and context limits (up to 1M), but lacks detail on the internal compute overhead of the extended thinking mode.

Anthropic uses clear semantic-style versioning for its API (e.g., claude-sonnet-4-20250514) and maintains a public changelog for major updates. It provides deprecation notices for older models (e.g., the transition from 3.5 to 4.5). While some behavioral drift is inevitable with safety updates, the company is relatively transparent about model retirements and the availability of specific snapshots.