o3-mini vs Claude 3.5 Haiku

1. High-intelligence small reasoning model

• Delivers strong reasoning performance in a compact footprint.
• Ideal for tasks that need intelligence but must stay cost-efficient.

2. Excellent for developer workflows

• Supports Structured Outputs, function calling, and Batch API.
• Reliable for backend automation, agents, and data-processing pipelines.

3. Strong text reasoning capabilities

• Handles multi-step logic, natural language analysis, SQL translation, entity extraction, and content generation.
• Works well for landing pages, policy summaries, and knowledge extraction (as shown in built-in examples).

4. 200K context window

• Allows large documents, multi-step analysis, and long-running conversations.
• Reduces the need for aggressive chunking or external retrieval systems.

5. High 100K-token output limit

• Enables long explanations, multi-section documents, or detailed reasoning sequences.

6. Pure text-focused model

• Input/output is text-only (no image or audio support).
• Optimized for language-heavy reasoning and logic tasks.

7. Broad API compatibility

• Works across Chat Completions, Responses, Realtime, Assistants, Embeddings, Image APIs (as tools), and more.
• Supports streaming, function calling, and structured outputs.

8. Cost-efficient for production at scale

• Same cost/performance profile as o1-mini but with higher intelligence.

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