How we benchmarked
We ran 600+ evals across tool use, retrieval, structured outputs, and safety prompts with production-like flows (n8n, Make, and LangChain tool calls). We measured latency, refusal rates, and hallucination under constrained sources.
Where GPT-5.3 wins
- Complex toolchains with 3–5 calls (CRM, billing, email) where precise JSON is required.
- Latency-sensitive flows (lead routing, live support suggestions) when tuned with concise prompts.
- Code-heavy tasks and structured transformations.
Where Claude 4.6 wins
- Policy-heavy reasoning and summarization with lower hallucination risk.
- Long-context analysis (playbooks, SOPs) at lower cost.
- Default safety: stricter refusals reduce risk for sensitive domains.
How we route in production
Cost and latency snapshots
Claude 4.6 often lands 30–40% cheaper for long-context reasoning. GPT-5.3 is 5–15% faster on short prompts. We set budgets and latency SLOs per workflow, then route dynamically.
Safety and governance
We enforce refusal rules, source grounding, and audit logs for both. Claude starts conservative; GPT-5.3 can match with explicit policies. Sensitive data? We default to private or retrieval-only paths.
Bottom line
Use GPT-5.3 when speed and tool precision matter. Use Claude when policy, summarization, or long context dominate. Mix them with routing, and keep safety/grounding non-negotiable.
FAQ
Rarely for automation. We prefer prompt+retrieval and small adapters; keeps cost and risk down.
Both perform well; GPT-5.3 edges slightly. We still localize knowledge and refuse outside supported languages.
For sensitive data, we pair open-source LLMs with retrieval and keep cloud models away from raw records.
Quarterly or before major model upgrades. We keep regression suites to avoid surprises.