Claude Sonnet 4.5 vs GPT-5.4

On shared benchmark coverage, MMLU PRO has Claude Sonnet 4.5 at 86 and GPT-5.4 at 87.5, with GPT-5.4 ahead by 1.5 points; Massive Multi-discipline Multimodal Understanding has Claude Sonnet 4.5 at 77.8 and GPT-5.4 at 82.1, with GPT-5.4 ahead by 4.3 points. The largest visible gap is 4.3 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 and code execution: GPT-5.4. Both models share multimodal input, reasoning mode, function calling, and tool use, 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 GPT-5.4 lists $2.5/1M input and $15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GPT-5.4 lower by about $0.35 per million blended tokens. Availability is 8 providers versus 2, so concentration risk also matters.

Choose Claude Sonnet 4.5 when vision-heavy evaluation and broader provider choice are central to the workload. Choose GPT-5.4 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.