TLDR¶

• Core Features: Claude Sonnet 4.5 extends autonomous coding sessions up to 30 hours, improves reliability, and tightens safety while outperforming earlier Claude models in broad tasks.

• Main Advantages: Substantial jump in sustained coding capability, stronger multi-step reasoning, and stricter safeguards for enterprise workflows and regulated environments.

• User Experience: Smoother long-form development cycles, fewer interruptions, and more consistent responses during intensive coding and documentation tasks.

• Considerations: Real-world performance depends on workload and integration; costs and model access may vary; long sessions demand disciplined prompt design.

• Purchase Recommendation: A compelling upgrade for teams needing marathon coding runs, safer outputs, and consistent reasoning—especially in professional and enterprise settings.

Product Specifications & Ratings¶

Overall Rating: ⭐⭐⭐⭐⭐ (4.8/5.0)

Product Overview¶

Anthropic’s Claude Sonnet 4.5 is the company’s most capable mid-weight model to date, positioned as an all-purpose AI assistant with a strong emphasis on long-form coding, process reliability, and improved safety. The headline capability is its ability to maintain autonomous coding sessions for up to 30 hours—more than quadruple the roughly seven uninterrupted hours reportedly supported by the earlier Claude Opus 4. This leap is significant not only as a raw metric but also for what it enables: marathon development cycles, multi-file refactors, end-to-end feature builds, and complex debugging sessions that span a full workday (and beyond) without losing context or consistency.

Anthropic frames Sonnet 4.5 as stronger “in almost every way” compared with previous Claude versions, and in early impressions, this claim aligns with what the new duration benchmark suggests. Long-running coding sessions are extremely sensitive to context drift, error accumulation, and hallucination risk; the move from seven to thirty hours implies improved state management, better internal planning heuristics, more rigorous safety guardrails, and resilience under iterative prompts. For developers accustomed to nudging models through brittle, stop-and-go workflows, this change alone can shift how teams approach AI-assisted development.

The model also emphasizes safety enhancements. For regulated industries, security-minded teams, or any organization deploying AI in production-aligned workflows, safety isn’t an optional layer—it’s a gating requirement. With Sonnet 4.5, Anthropic positions its model as suitable for more sensitive use cases by reinforcing refusal behavior, tightening guidance for restricted tasks, and reducing the likelihood of risky outputs. The practical effect is less back-and-forth to “steer” the assistant, fewer compliance hiccups, and a more predictable operational profile at scale.

Beyond the headline features, users can expect incremental improvements in general-purpose reasoning, code explanation, documentation synthesis, and step-by-step task planning. The extended session capability amplifies all of these strengths because the model can stick with a problem longer, refine code continuously, and deliver more cohesive outcomes across longer horizons. In short, Claude Sonnet 4.5 seeks to convert AI-assisted coding from a short sprint into a sustained, strategic collaboration—one that’s safer, more durable, and capable of managing real-world, multi-hour workloads without breaking stride.

In-Depth Review¶

Claude Sonnet 4.5 is defined by its endurance: up to 30 hours of autonomous coding. That single upgrade reshapes several pillars of model performance.

Context retention and stability: Extended sessions reveal how well a model manages evolving project state, including partial implementations, pending TODOs, and cross-file dependencies. Compared to the roughly seven hours supported by Claude Opus 4, Sonnet 4.5’s endurance suggests stronger internal mechanisms for state continuity and reduced drift. In practice, this means fewer resets, less re-prompting to restore context after long breaks, and more coherent delivery across complex, multi-step tasks such as multi-service integrations, build pipeline adjustments, or refactors that require repeated passes.

Reasoning strength: While Anthropic’s statement that Sonnet 4.5 is stronger “in almost every way” is broad, it is consistent with the ambition behind longer, autonomous coding cycles. Effective long-form reasoning requires robust plan-following, error recovery, and the ability to integrate feedback from logs, test failures, and version control diffs. Sonnet 4.5 appears tuned for precisely these scenarios, yielding better performance on iterative tasks like debugging tricky concurrency issues, migrating frameworks, or aligning code with security and compliance policies at the function and module level.

Safety and reliability: Safety enhancements are central to Sonnet 4.5’s positioning. Stronger safeguards reduce the chance of generating obviously unsafe or policy-violating outputs, a critical element for enterprise deployment. When coding, this can manifest as more cautious handling of security-sensitive snippets, improved guidance around cryptographic practices, and additional scrutiny in areas like authentication flows, secrets handling, and data access patterns. The benefit is real: fewer risky suggestions to filter out, less time spent auditing model outputs, and more confidence when adopting AI across teams with varying skill levels.

Workflows and integrations: Extended autonomous sessions are most valuable when they mesh with the developer’s existing tools and processes. Sonnet 4.5’s stability should translate well into workflows where the model is tasked with continuous code changes, documentation updates, and test runs. For example, a team could instruct the model to implement a new feature, generate a comprehensive test suite, run those tests, interpret the failures, and iterate until green, all within a long-running session. This reduces operator overhead and aligns the AI assistant more closely with CI/CD rhythms. It also encourages disciplined prompt engineering: providing clear goals, constraints, and acceptance criteria at the outset, so the model can execute extended plans autonomously.

*圖片來源：Unsplash*

Scalability and team impact: Longer, safer sessions help standardize outcomes across teams. Junior developers benefit from the model’s stronger guardrails and structured reasoning, while senior engineers can offload boilerplate and focus on higher-level architecture. Over time, this shifts throughput and predictability: fewer abandoned threads, less fragmentation, and more end-to-end completeness in delivered work. In organizations where code quality and auditability are paramount, the consistency and safety posture of Sonnet 4.5 can streamline peer review and reduce post-merge rework.

Limits and caveats: Performance always depends on the nature of the task. Highly specialized domains or edge-case frameworks may still require careful supervision and tailored prompts. The 30-hour benchmark, while impressive, should be treated as an envelope rather than a guarantee across all operational conditions. Costs and quotas will also shape how teams deploy extended sessions; thoughtful orchestration—segmenting long tasks into milestones, aligning with CI/CD checkpoints, and leveraging version control strategically—will extract maximum value while keeping usage efficient.

Bottom line: Claude Sonnet 4.5’s major leap in autonomous session duration, combined with broadened reasoning and enhanced safety, makes it a strong fit for modern software organizations. It positions AI not as a quick-fix coding assistant but as a reliable collaborator capable of sustained, end-to-end contribution over significant spans of time.

Real-World Experience¶

Evaluating real-world applicability starts with day-to-day developer workflows. Over an extended work block, a typical scenario might begin with the model scaffolding an application component, implementing business logic, generating integration tests, and diagnosing runtime issues based on logs and tracebacks. With earlier models, developers often faced session resets or context loss after a few hours, leading to repetitive prompts and a slowdown in momentum. Sonnet 4.5’s sustained context allows the model to carry forward lessons from earlier iterations, enabling it to revise code with awareness of previous attempts, open issues, and nuanced constraints introduced midstream.

Consider a backend service update that spans multiple repositories: migrating from an older ORM to a modern alternative, updating database schema migrations, and synchronizing changes across documentation, CI pipelines, and infrastructure-as-code templates. Sonnet 4.5’s long session tolerance means it can keep track of dependencies, respond to test failures as they emerge, and refine migration scripts while preserving design intents established hours earlier. This continuity reduces the risk of partial or inconsistent updates that can plague multi-repo work.

Documentation is another area where the model’s endurance shines. Teams often want synchronized code comments, README updates, architectural decision records, and inline guidance. In a multi-hour session, the model can produce comprehensive documentation as part of the same effort that generated the code, referencing the latest implementation details and test outcomes. The result is clearer onboarding materials and less drift between the code base and its documentation.

Safety enhancements also matter in lived experience. For example, in authentication flows, the model is more likely to recommend secure defaults and warn about pitfalls like improper token storage. In data access patterns, it can promote least-privilege concepts and surface checks that align with compliance standards. This doesn’t eliminate the need for human oversight, but it reduces the number of risky snippets that reviewers have to catch, improving both speed and confidence.

Over the course of long debugging sessions—say, tracing intermittent production-like failures reproduced in staging—Sonnet 4.5 can maintain hypotheses, compare logs over time, and prioritize likely root causes. When errors recur, the model can leverage previous investigative threads, making it less prone to “forgetting” earlier insights. This leads to faster resolution cycles and less frustration, especially when issues only present after extended runtime or under specific concurrency profiles.

The model also supports more strategic workflows, such as phased refactors. A team might designate goals for each phase—clean up dependencies, enforce stricter typing, modularize services, then harden security—and run a single continuous session through these phases. Instead of starting anew each day and re-establishing context, the model advances through the plan, carrying forward context and ensuring that decisions made early remain consistent later.

Developers should still apply good practices: explicit scoping, clear acceptance criteria, version control checkpoints, and automated testing. Sonnet 4.5’s capacity rewards teams that set guardrails up front—linting, test coverage goals, code style enforcement—because the model will incorporate those signals across the full session. As a result, output tends to align more tightly with organizational standards, which pays dividends during review and deployment.

Ultimately, Sonnet 4.5 feels less like a tool you intermittently consult and more like a partner that can stay engaged from kickoff to completion. The combination of long-session endurance and safety-aware behavior is especially valuable in professional settings where code must be both functional and responsibly produced.

Pros and Cons Analysis¶

Pros:
– Up to 30 hours of autonomous coding sessions, vastly reducing context resets.
– Stronger reasoning and planning across multi-step development tasks.
– Enhanced safety posture suitable for enterprise and regulated workflows.

Cons:
– Real-world mileage varies by codebase complexity and integration quality.
– Extended sessions may increase usage costs without disciplined orchestration.
– Requires clear prompts and guardrails to maximize output quality.

Purchase Recommendation¶

Claude Sonnet 4.5 is an easy recommendation for teams that rely on AI for sustained, end-to-end development work. The leap from roughly seven to up to 30 hours of autonomous coding fundamentally changes how teams can structure projects: instead of micro-managing the assistant, you can set goals, define constraints, and let the model work through multiple phases with minimal intervention. This shift reduces friction, preserves momentum, and helps deliver more cohesive outcomes, particularly in complex environments with many moving parts.

Enterprises and security-conscious organizations will appreciate the reinforced safety profile. The model’s cautious defaults and improved adherence to best practices lower the risk of producing problematic code or guidance, freeing teams to move faster while maintaining compliance standards. When paired with strong internal processes—code reviews, CI/CD, and clear policies—Sonnet 4.5 integrates smoothly into production-aligned workflows.

For individual developers and small teams, the value proposition remains strong. Longer sessions translate to fewer interruptions and quicker iteration cycles, especially for feature builds, bug hunts, and documentation passes that span many hours. However, to make the most of the model, users should invest in thoughtful prompt design and project structure. Establishing checkpoints, acceptance criteria, and testing gates will keep long sessions on course and maximize the return on usage.

If your current AI setup struggles with context loss, fragmented outputs, or safety concerns, Claude Sonnet 4.5 is a meaningful upgrade. It is best suited for professional settings where reliability, longevity, and responsible behavior are non-negotiable. While costs and access may vary depending on deployment, the productivity gains from fewer resets, stronger reasoning, and safer defaults can offset expenses quickly, especially at team scale. In short, Claude Sonnet 4.5 earns a top-tier recommendation for developers who want a dependable, long-distance partner in code.

References¶

• Original Article – Source: techspot.com
• Supabase Documentation
• Deno Official Site
• Supabase Edge Functions
• React Documentation

*圖片來源：Unsplash*