Claude 4.5 Haiku vs Claude 4.5 Opus

1. Frontier-level coding at small-model speed

• Similar coding performance to Claude Sonnet 4 at one-third the cost.
• Runs 4-5x faster than Sonnet 4.5 for many tasks.
• Ideal for real-time pair programming, prototyping, and rapid iteration.

2. Excellent computer-use abilities

• Surpasses Claude Sonnet 4 in certain computer-control tasks.
• Great for agents requiring low-latency tool use (Chrome automation, coding agents, etc.).

3. Perfect for real-time, low-latency applications

• Chat assistants
• Customer support agents
• Interactive development loops
• Multi-agent orchestration

4. Works seamlessly with Sonnet 4.5 in hybrid agent setups

• Sonnet 4.5 plans complex workflows.
• Haiku 4.5 executes subtasks in parallel for speed and cost-efficiency.

5. High alignment & safest Claude model by metric

• Lower misaligned behavior rates than Haiku 3.5, Sonnet 4.5, and Opus 4.1.
• Limited CBRN risk → released under AI Safety Level 2 (ASL-2).

1. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.