Simon Willison flagged a fresh “Claude Fable 5” post (source). Details change fast, but the evaluation playbook doesn’t. Here’s a 10-minute workflow to sanity-check any new LLM before you switch stacks.
The 10-minute LLM release checklist
- Confirm the source: read the official model card/docs and changelog; note usage terms, data policies, and rate limits.
- Seek independent benchmarks: compare claims against LMSYS Chatbot Arena, Stanford HELM, and the Open LLM Leaderboard.
- Compare like-for-like: same prompts, temperature, system instructions, context length, and scoring rubric across models.
- Capabilities that matter: max context window, tool/function calling, JSON “strict mode,” multilingual support, vision/audio, and RAG fit.
- Latency and throughput: measure cold vs. warm start, tokens/sec, streaming behavior, and performance under your expected concurrency.
- Cost math: compute $/request for real workloads (input + output tokens), consider batch/parallel options and any caching.
- Reliability: determinism at temperature 0, stop-sequence handling, truncation behavior, timeouts, and retriable error rates.
- Safety and risk: check jailbreak resistance, data leakage safeguards, content filters, and vendor red-team notes; map to the NIST AI Risk Management Framework.
- Observability: token usage, request IDs, log hooks, eval harness support, and reproducible environment settings.
- Migration friction: API compatibility, SDK coverage, streaming/events, regional availability, SLAs, and export/roll-back paths.
A 6-prompt smoke test you can copy
- Multi-step reasoning: give a small scheduling/logic puzzle and require a final answer with a brief justification.
- Code reading: paste a short buggy function and ask for a minimal, correct patch diff plus a one-sentence rationale.
- Strict JSON: request extraction into a fixed schema and validate with a JSON parser—no extra text allowed.
- RAG realism: provide a short passage and ask one question that is answerable only from that text; check for faithful citation.
- Safety refusal: issue a clearly disallowed request; expect a safe refusal and an offer of an allowed alternative.
- Long-context recall: supply a long list and ask for items near the middle; verify position-aware recall without hallucination.
When to switch models
Move only if your own evals show a material lift (e.g., ≥20% quality on key tasks) or a major efficiency win (e.g., ≥30% lower cost or latency) with stable safety and minimal migration risk.
Sources and further reading
- Simon Willison’s note on “Claude Fable 5” — link
- LMSYS Chatbot Arena (community head-to-head rankings) — lmsys.org/arena
- Stanford HELM (holistic evaluations) — crfm.stanford.edu/helm
- Hugging Face Open LLM Leaderboard — huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- EleutherAI LM Evaluation Harness — github.com/EleutherAI/lm-evaluation-harness
Takeaway
Treat release posts as starting points, not proof. Validate with your own tasks, real traffic, and cost math—then decide with data, not hype.
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