Claude Sonnet 4.5 vs o3

On shared benchmark coverage, Massive Multi-discipline Multimodal Understanding has Claude Sonnet 4.5 at 77.8 and o3 at 82.9, with o3 ahead by 5.1 points. The largest visible gap is 5.1 points on Massive Multi-discipline Multimodal Understanding, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on vision: Claude Sonnet 4.5, multimodal input: Claude Sonnet 4.5, function calling: Claude Sonnet 4.5, tool use: Claude Sonnet 4.5, and code execution: o3. Both models share reasoning mode and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Claude Sonnet 4.5 lists $3/1M input and $15/1M output tokens, while o3 lists $1/1M input and $4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o3 lower by about $4.7 per million blended tokens. Availability is 8 providers versus 3, so concentration risk also matters.

Choose Claude Sonnet 4.5 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose o3 when coding workflow support and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.