A new flagship LLM drops. Before you rebuild your stack, run this 10-minute checklist. We use Simon Willison’s coverage of Claude Opus 5 as a jumping-off point, focusing on what to verify quickly and what to test with your own data.
The 10-minute LLM release checklist
- Confirm what’s truly new: context window, tool/function calling, structured JSON, vision, embeddings, or agentic features.
- Scan pricing and performance: input/output token costs, throughput (tokens/sec), max rate limits, and latency tiers.
- Run smoke tests on your prompts: math, long-context retrieval, tool-use robustness, and JSON validity.
- Compare to your baseline model on the same tasks to avoid benchmark bait.
- Probe reasoning claims with adversarial prompts and hidden tests; watch for confident mistakes.
- Check multilingual and domain terms (jargon, acronyms) relevant to your users.
- Evaluate refusal/safety behavior and jailbreak resistance for your use case.
- Validate API ergonomics: SDK parity, streaming, batch jobs, and retries/timeouts.
- Review the model card and third-party evaluations critically; look for test sets you can reproduce.
- Pilot behind a feature flag; capture win-rate, cost per action, latency, and failure modes before scaling.
How to verify claims fast
Start with repeatable prompts you already trust. Keep a small rubric: exactness, reasoning steps, citations, and JSON validity. Score quickly and compare to your baseline.
For long-context claims, build a 30–50 page synthetic doc with a few buried needles. Ask for precise quotes and page refs. For tool-use, intentionally return API errors and malformed JSON to test recovery.
Cost, speed, and context trade-offs
Big context windows can hide latency and cost spikes. Measure tokens generated per second and total tokens per task. Track cost per solved task, not just per 1K tokens.
If you ship internationally or across devices, test on mobile networks. Throughput and streaming stability can make or break UX even when quality looks similar.
Safety and reliability
Run a quick red-team set focused on your domain (prompt injections in RAG, brand-risk topics, PII handling). Validate refusal consistency, not just “will it refuse once.”
For structured output, require strict JSON mode (if available) and enforce schemas. Add guardrails that fail fast and retry with system prompts or function calls.
Useful sources
- Simon Willison’s write-up for quick context: Introducing Claude Opus 5.
- Independent evaluation guidance: Stanford HELM on reproducible LLM benchmarks.
- Model docs and pricing (example): Anthropic model documentation.
Takeaway
Don’t chase headline benchmarks. In 10 minutes you can validate claims on your data, quantify cost/latency, and decide whether to pilot—before committing engineering time.
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