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LLM Comparisono4-miniClaude 3.5 Sonnet

o4-mini vs Claude 3.5 Sonnet

Compare o4-mini and Claude 3.5 Sonnet. Build AI products powered by either model on Appaca.

Model Comparison

Featureo4-miniClaude 3.5 Sonnet
ProviderOpenAIAnthropic
Model Typetexttext
Context Window200,000 tokens200,000 tokens
Input Cost
$1.10/ 1M tokens
$3.00/ 1M tokens
Output Cost
$4.40/ 1M tokens
$15.00/ 1M tokens

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Strengths & Best Use Cases

o4-mini

OpenAI

1. Fast and efficient reasoning

  • Provides strong reasoning capabilities with significantly lower latency and cost compared to larger o-series models.
  • Ideal for lightweight reasoning tasks, logic steps, and quick multi-step thinking.

2. Optimized for coding tasks

  • Performs exceptionally well in code generation, debugging, and explanation.
  • Useful for IDE integrations, coding assistants, and developer tools with tight latency budgets.

3. Strong visual reasoning

  • Accepts image inputs for tasks such as diagram interpretation, charts, UI analysis, and visual logic.
  • Great for hybrid text-image reasoning flows.

4. Large 200K-token context window

  • Capable of processing long documents, multi-file codebases, or extended analysis.
  • Reduces need for chunking or external retrieval pipelines.

5. High 100K-token output limit

  • Supports lengthy reasoning sequences, full codebase explanations, or multi-section documents.

6. Broad API compatibility

  • Available in Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, and Image workflows.
  • Supports streaming, function calling, structured outputs, and fine-tuning.

7. Cost-efficient for production

  • Lower input/output pricing makes it suitable for large-scale deployments, SaaS products, and recurring tasks.

8. Succeeded by GPT-5 mini

  • GPT-5 mini offers improved speed, reasoning power, and pricing, but o4-mini remains a strong option for cost-sensitive workloads.

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.