Claude Sonnet 4.5 vs o3

On shared benchmark coverage, SWE-bench Verified has Claude Sonnet 4.5 at 77.2 and o3 at 71.7, with Claude Sonnet 4.5 ahead by 5.5 points; Google-Proof Q&A has Claude Sonnet 4.5 at 83.4 and o3 at 87.7, with o3 ahead by 4.3 points; 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 5.5 points on SWE-bench Verified, 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 code execution: o3. Both models share vision, multimodal input, reasoning mode, and function calling, 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 tiered pricing: 0-200,001t is $3/1M input and $15/1M output; 200,001t+ is $6/1M input and $22.50/1M output, while o3 lists $2/1M input and $8/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o3 lower by about $2.80 per million blended tokens. For tiered rows, this cheapest-track view can understate interactive or fast-lane spend, so compare the tier you will actually use. Availability is 8 providers versus 3, so concentration risk also matters.

Choose Claude Sonnet 4.5 when vision-heavy evaluation 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.