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LLM ComparisonClaude 4.5 HaikuClaude 3.5 Sonnet

Claude 4.5 Haiku vs Claude 3.5 Sonnet

Compare Claude 4.5 Haiku and Claude 3.5 Sonnet. Build AI products powered by either model on Appaca.

Model Comparison

FeatureClaude 4.5 HaikuClaude 3.5 Sonnet
ProviderAnthropicAnthropic
Model Typetexttext
Context Window200,000 tokens200,000 tokens
Input Cost
$1.00/ 1M tokens
$3.00/ 1M tokens
Output Cost
$5.00/ 1M tokens
$15.00/ 1M tokens

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Strengths & Best Use Cases

Claude 4.5 Haiku

Anthropic

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).

Claude 3.5 Sonnet

Anthropic

1. Intelligence & Reasoning

  • Outperforms previous Claude models and competitor LLMs across major benchmarks.
  • Excels in graduate-level reasoning (GPQA), knowledge tasks (MMLU), and coding (HumanEval).
  • Handles nuance, humor, and complex instructions with human-like clarity.

2. Speed & Efficiency

  • Runs 2x faster than Claude 3 Opus, making it ideal for real-time and high-volume workflows.
  • Cost-effective pricing: $3/M input tokens and $15/M output tokens.
  • Supports a 200K token context window, enabling rich, long-form reasoning.

3. Coding Capabilities

  • Solves significantly more coding and bug-fix tasks (64% vs Opus's 38% in internal evaluations).
  • Can autonomously write, edit, and execute code when tool use is enabled.
  • Strong at translating and modernizing legacy codebases.

4. Vision Strength

  • Best vision model in the Claude family, surpassing Opus on vision benchmarks.
  • Excellent at interpreting charts, graphs, and imperfect images.
  • Reliable text extraction from low-quality visuals for retail, logistics, finance, etc.

5. Agentic Workflows

  • Highly capable for multi-step task orchestration.
  • Performs well as the engine for agents requiring reasoning, planning, and tool-calling abilities.

6. Content Quality

  • Produces natural, relatable writing with improved tone, style, and context awareness.
  • Strong at long-form content creation and editing.

7. Safety & Reliability

  • Rated ASL-2, meeting Anthropic's safety standards.
  • Undergoes extensive red-teaming and external evaluation (UK AISI & US AISI).
  • Not trained on user data without explicit permission.

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