Claude 4.5 Sonnet vs Claude 3.5 Haiku

1. Best-in-class coding performance

• #1 on SWE-bench Verified (77.2% standard, 82.0% high-compute).
• Excels at debugging, architecture, and multi-file code generation.
• Maintains coherence for extremely long tasks (30+ hours).

2. State-of-the-art computer use & agents

• Leads OSWorld at 61.4%.
• Strongest model for agentic workflows, multi-step tool use, and real computer control.
• Powering Claude Code, the new Claude Agent SDK, and Chrome agent actions.

3. Advanced reasoning & math

• Large improvements across reasoning-heavy benchmarks (AIME, MMMLU, τ2-bench, Terminal-Bench).
• Deep multi-step reasoning with extended or interleaved thinking.

4. High alignment & safety

• Most aligned Claude model to date with reduced deception, hallucinations, sycophancy, and harmful compliance.
• Strong protections against prompt injection for agentic tasks (ASL-3 safeguards).

5. Domain-expert performance

• Notable gains in finance, law, medicine, and STEM tasks.
• Trusted by early customers for long-context legal analysis, multi-file engineering, security research, and red-teaming.

1. Intelligence & Benchmark Performance

• Matches Claude 3 Opus (previous largest model) on many intelligence tasks.
• Surpasses Claude 3 Opus on multiple evaluations despite being a smaller, faster model.
• Major improvements across every skill category vs previous Haiku.

2. Coding Strength

• Scores 40.6% on SWE-bench Verified, outperforming:

  • Claude 3.5 Sonnet (original version)
  • GPT-4o
  • Many agent-driven systems

• Excellent for engineering assistants, agent coding tasks, and bug fixing.

3. Speed & Latency

• Same speed class as Claude 3 Haiku (ultra-fast).
• Ideal for real-time interactions, high request volumes, and UI responsiveness.

4. Tool Use & Instruction Following

• Better at following instructions than previous Haiku.
• Stronger at tool use accuracy, making it reliable for agents and workflows.

5. Best Use Cases

• High-volume, low-latency tasks
• User-facing products
• Sub-agent tasks in larger workflows
• Processing large structured datasets (pricing, inventory, purchase history)
• Rapid content or code generation where speed matters