Claude 4.5 Opus Medium Effort

Anthropic identifies Claude 4.5 Opus as a 'hybrid reasoning' model, a dense transformer architecture that integrates both direct inference and chain-of-thought (CoT) capabilities. While the 'effort' parameter (low, medium, high) is publicly documented as a mechanism to control computational intensity and reasoning depth, the underlying architectural specifics—such as layer counts, attention head configurations, or the exact nature of the 'hybrid' integration—remain undisclosed. Documentation describes the model as a successor to the Claude 3.7/4.0 lineage but lacks the technical depth required for a high score.

Information regarding the training data for Claude 4.5 Opus is extremely limited. Official communications mention a 'reliable knowledge cutoff' of March 2025 and state the model is trained on a 'diverse' set of data including web, code, and books. However, there is no public disclosure of specific dataset proportions, source lists, or detailed cleaning and filtering methodologies. The reliance on vague descriptions like 'high-quality data' without verifiable composition metrics results in a low score.

The model uses a tokenizer consistent with the Claude 3 and 4 families, supporting a 200,000-token context window and a 64,000-token output limit. While the tokenizer is accessible via the Anthropic API and third-party libraries (e.g., tiktoken-compatible wrappers), official technical documentation detailing the specific vocabulary size, training alignment, or normalization procedures for the 4.5 Opus variant is not comprehensively provided in a standalone technical paper.

The total parameter count for Claude 4.5 Opus is not publicly disclosed. Although the model is described as a 'dense' architecture, there is no information regarding the number of active parameters or a breakdown of architectural components (e.g., FFN vs. attention). The lack of even approximate parameter figures or density metrics makes this aspect of the model opaque.

There is virtually no public information regarding the compute resources used to train Claude 4.5 Opus. Details such as GPU/TPU hours, hardware specifications, training duration, and carbon footprint are entirely absent from official documentation. Anthropic does not provide any environmental impact data or cost estimates for the training phase.

Anthropic provides scores for several major benchmarks, including SWE-bench Verified (80.9% at high effort), Terminal-bench 2.0 (59.3%), and OSWorld (66.3%). While these scores are compared against competitors like GPT-5.1 and Gemini 3 Pro, the company does not release the full evaluation code, exact prompts, or few-shot examples required for independent reproduction. Third-party analysis from platforms like Artificial Analysis provides some external validation, but the lack of official reproduction instructions limits transparency.

The model consistently identifies itself as Claude 4.5 Opus and is aware of its versioning and the 'effort' parameter functionality. It correctly distinguishes its capabilities from other models in the 4.5 family (Sonnet and Haiku) and accurately reflects its knowledge cutoff. Minor points are deducted because the model occasionally expresses internal uncertainty about its precise capability delta compared to previous versions due to a lack of 'internal documentation' access during inference.

Claude 4.5 Opus is a proprietary, closed-weights model. Access is provided solely through Anthropic's API and commercial platforms (AWS Bedrock, Google Cloud Vertex AI). The license terms are restrictive and governed by Anthropic's Terms of Service, which do not meet open-source standards. While the commercial usage terms are clear for API customers, the lack of an open license for weights or code results in a low transparency score.

As a closed-source API-based model, there is no public documentation regarding the hardware requirements (VRAM, RAM) for local deployment. While Anthropic provides latency estimates for different effort levels (e.g., 30-60s for high effort), it does not disclose the underlying infrastructure requirements or the impact of quantization on model performance, as these are managed entirely server-side.

Anthropic uses clear model identifiers (e.g., 'claude-opus-4-5-20251101') and maintains a changelog for its API. The introduction of the 'effort' parameter provides a structured way for users to manage performance-cost tradeoffs. However, the company has a history of 'silent' updates to safety filters and alignment layers that can affect model behavior without a version increment, and previous versions are not always indefinitely accessible.