o3-mini vs Claude 4.5 Opus

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. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.