Claude Sonnet 4.5 vs GPT-5
A side-by-side comparison of two multimodal models — real specs, pricing, strengths and weaknesses, and a clear verdict on which to choose. Kept current by our agents.
Quick verdict: which should you choose?
Choose Claude Sonnet 4.5 if you need
- ✓Choose Claude Sonnet 4.5 if you need reliable handling of 1M-token contexts for large-scale multimodal analysis.
- ✓Choose Claude Sonnet 4.5 if you need strong safety alignment and careful detailed reasoning.
- ✓Choose Claude Sonnet 4.5 if you need effective multimodal file support with verified performance.
- ✓Choose Claude Sonnet 4.5 if you need to avoid unverified hypothetical models.
Choose GPT-5 if you need
- ✓Choose GPT-5 if you need lower output pricing at $10 per million tokens.
- ✓Choose GPT-5 if you need documented output speed of 172.76 t/s.
- ✓Choose GPT-5 if you need native multimodal input with seamless text-image-file integration.
- ✓Choose GPT-5 if you need a 400k-token context for complex inputs at reduced cost.
Verdict
Claude Sonnet 4.5 leads in verified large-scale multimodal reasoning with a 1M-token context and strong safety alignment, while GPT-5 offers lower pricing at $10 per million tokens and a documented 172.76 t/s speed but remains a hypothetical model with unverified performance. GPT-5's 400k context is smaller yet supports seamless native multimodal integration. Claude's reliable handling of large contexts gives it an edge for complex verified workloads where GPT-5's resource demands and latency risks are drawbacks.
Claude Sonnet 4.5 vs GPT-5: side by side
| Spec | Claude Sonnet 4.5 | GPT-5 | Winner |
|---|---|---|---|
| Intelligence | — | 15.3 | Tie |
| Output speed | — | 167 t/s | Tie |
| Output price | $15.00/1M | $10.00/1M | GPT-5 |
| Context | 1000K | 400K | Claude Sonnet 4.5 |
| Params | — | — | Tie |
| Provider | Anthropic | OpenAI | Tie |
Detailed analysis
Context Window
Winner: Claude Sonnet 4.5Claude Sonnet 4.5 provides a 1,000,000-token context compared to GPT-5's 400,000 tokens. This gives Claude a clear advantage for large-scale multimodal reasoning tasks requiring extensive context. GPT-5's smaller window still supports complex inputs but limits maximum scale.
Pricing
Winner: GPT-5GPT-5 is priced at $10 per million output tokens versus Claude Sonnet 4.5 at $15 per million. The $5 difference favors GPT-5 for high-volume usage. Both are proprietary models from established providers.
Speed and Verification
Winner: GPT-5GPT-5 reports a specific output speed of 172.76 t/s while Claude Sonnet 4.5 has no speed figure provided. However GPT-5 is explicitly described as a hypothetical model with unverified performance, creating uncertainty. Claude offers verified strengths in reliable large-context handling.
Multimodal Capabilities
Winner: TieBoth models support multimodal inputs with Claude emphasizing effective file support and careful reasoning while GPT-5 highlights native seamless text-image-file integration. Claude's vision performance varies by image complexity and GPT-5 notes potential latency on large tasks. Strengths are described differently but neither dominates on available facts.
Claude Sonnet 4.5
Pros
- +Strong safety alignment
- +Reliable handling of large contexts
- +Careful and detailed reasoning
Cons
- –Can be overly cautious with refusals
- –Vision performance varies by image complexity
- –May produce verbose responses
GPT-5
Pros
- +Very large context window
- +Native multimodal input support
- +Seamless text-image-file integration
Cons
- –Hypothetical model with unverified performance
- –High resource demands for maximum context
- –Potential latency on large multimodal tasks
Summary: Claude Sonnet 4.5 vs GPT-5
Select Claude Sonnet 4.5 for verified large-context multimodal work needing safety and reliability. Choose GPT-5 when lower price and documented speed matter more despite its hypothetical status. The models trade off context size against cost and verification.
Frequently asked questions
Claude Sonnet 4.5 is better for verified large-scale multimodal reasoning with 1M context while GPT-5 suits cost-sensitive needs but carries unverified performance limitations.