LLM ComparisonClaude 4.6 OpusClaude 3 Haiku

Claude 4.6 Opus vs Claude 3 Haiku

Compare Claude 4.6 Opus and Claude 3 Haiku. Build AI products powered by either model on Appaca.

Model Comparison

FeatureClaude 4.6 OpusClaude 3 Haiku
ProviderAnthropicAnthropic
Model Typetexttext
Context Window1,000,000 tokens200,000 tokens
Input Cost
$5.00/ 1M tokens
$0.25/ 1M tokens
Output Cost
$25.00/ 1M tokens
$1.25/ 1M tokens

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Strengths & Best Use Cases

Claude 4.6 Opus

Anthropic

1. Anthropic's top model for coding and agents

  • Anthropic positions Opus 4.6 as its most intelligent model for building agents and coding.
  • It builds on Opus 4.5 with higher reliability and precision for professional software engineering, complex agentic workflows, and high-stakes enterprise tasks.

2. Strong frontier performance on real agent benchmarks

  • Anthropic reports state-of-the-art results across coding and agentic evaluations.
  • Public benchmark highlights include 65.4% on Terminal-Bench 2.0, 72.7% on OSWorld, and 90.2% on BigLaw Bench.

3. Best fit for long-horizon, high-context work

  • Supports up to a 1M token context window in beta and up to 128K output tokens.
  • Designed for long-running tasks that need sustained planning, careful debugging, code review, and strong context retention.

4. Advanced reasoning controls and workflow support

  • Supports adaptive thinking and the effort parameter, including the new max effort level.
  • Anthropic also introduced fast mode, compaction, and dynamic filtering with web search and web fetch for Opus 4.6-era agent workflows.

Claude 3 Haiku

Anthropic

1. Speed

  • Fastest model in the Claude 3 family
  • Near-instant responses for chat, support, and live tools

2. Efficiency

  • Most affordable model
  • Ideal for massive-scale applications and low-latency use cases

3. Practical Use Cases

  • Customer support
  • Translations
  • Moderation
  • Logistics, inventory systems
  • Extracting insights from unstructured data

4. Vision Skills

  • Handles charts, images, and diagrams quickly
  • Useful for scanning large volumes of visual data

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