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Claude Opus 4.5 — November 2025

What Changed

Anthropic's Claude Opus 4.5 achieved state-of-the-art on software engineering benchmarks (SWE-bench Verified), with particular strength in multi-step agentic coding tasks. Designed for computer use — controlling browsers and GUIs autonomously — and long-running agents that must maintain coherent plans across many tool calls.

Key Technical Details

Constitutional AI at scale: Opus 4.5 continues Anthropic's RLAIF approach where model-generated critiques guided by written principles provide training signal without large-scale human labeling. The key advance: critique models specialized per domain (code review, safety, factual grounding) rather than a single general critic.

\[ \pi_{\theta}^{*} = \arg\max_\theta \mathbb{E}_{x \sim \pi_\theta}\bigl[r_\phi^{\text{domain}}(x) - \beta \log\frac{\pi_\theta(x)}{\pi_{\text{ref}}(x)}\bigr] \]

In Plain English

Each domain critic (code, factual, safety) provides its own reward signal. The policy maximizes a blend of domain-specific rewards while staying close to the reference model — so improvements are steered by AI-generated feedback aligned to principles, not only by human preference labels.

Technical Details
  • SWE-bench Verified: Anthropic claims SotA; measures a model's ability to resolve GitHub issues end-to-end in a sandboxed environment.
  • Computer use API: standardized interface for GUI actions (click, type, screenshot) — enables "agent as employee" workflows.
  • Pricing: \(5/\)25 per million input/output tokens.
  • Context: 200K token context window.

Practical Implications

For agentic coding, evaluate on task-level benchmarks (issue → patch → tests), not just HumanEval-style single-function completion. For computer use, run actions inside sandboxed VMs with network and filesystem policies; log every action for audit. For RLAIF, monitor reward hacking (critics agreeing with each other without grounding) and refresh principle sets as product risks evolve.

Interview Questions

  1. What is RLAIF (RL from AI Feedback) and how does it differ from RLHF? What are the scalability advantages and the risks?
  2. What does SWE-bench Verified measure that MMLU or HumanEval does not? Why is it a better proxy for real software engineering ability?
  3. What safety challenges are unique to computer use agents that are not present in chat-only deployments?

Code Example

Conceptual computer use loop (API shapes vary; consult Anthropic docs for exact schemas):

# Pseudocode: observe → act → observe until task done or step limit
state = env.reset()  # e.g. initial screenshot + accessibility tree
for step in range(max_steps):
    action = client.computer_use(model="claude-opus-4-5", state=state, goal=user_goal)
    state = env.step(action)  # click, type, scroll, wait, etc.
    if state.task_complete:
        break