1. Full-scale reasoning model

• Uses reinforcement learning to generate long internal chains of thought.
• Suitable for tasks requiring deep logic, multi-step planning, and rich analytical reasoning.

2. Strong performance across domains

• Excellent at math, science, coding, and structured analytical work.
• Handles multi-step workflows and complex problem-solving with high consistency.

3. High output capacity (100K tokens)

• Enables long, detailed explanations, large documents, and multi-part analyses.

4. Image-understanding capable

• Accepts text + image inputs for visual reasoning and mixed-modality tasks.
• Output is text only, optimized for clear explanations.

5. Advanced API compatibility

• Works with Chat Completions, Responses, Realtime, Assistants, and more.
• Supports streaming, function calling, and structured outputs.

6. Stable long-context performance

• 200K-token context window supports large files, multi-document analysis, and extended conversations.

7. Designed for correctness-oriented workloads

• Prioritizes rigorous reasoning over speed.
• Useful in auditing, verification, scientific thinking, policy analysis, and legal-style reasoning.

8. Powerful but expensive

• High token costs make it suitable for selective, mission-critical reasoning rather than high-volume usage.

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