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LLM ComparisonGPT-5 MiniClaude 3.5 Haiku

GPT-5 Mini vs Claude 3.5 Haiku

Compare GPT-5 Mini and Claude 3.5 Haiku. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5 MiniClaude 3.5 Haiku
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens200,000 tokens
Input Cost
$0.25/ 1M tokens
$0.80/ 1M tokens
Output Cost
$2.00/ 1M tokens
$4.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5 Mini

OpenAI

1. High reasoning performance

  • Retains strong reasoning capabilities despite being a smaller, faster model.
  • Suitable for tasks requiring accurate logic and structured thinking.

2. Fast and cost-efficient

  • Optimized for speed, making it ideal for real-time or high-volume workloads.
  • Far cheaper than GPT-5 while maintaining solid capability.

3. Great for well-defined tasks

  • Excels when prompts are precise and objectives are clearly specified.
  • More predictable and stable for deterministic workflows.

4. Multimodal input

  • Accepts text + image as input.
  • Outputs text only.

5. Tool support

  • Works with Web Search, File Search, Code Interpreter, MCP.
  • (Does not support Image Generation as a tool and does not support Computer Use.)

Claude 3.5 Haiku

Anthropic

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