LLM Model Selection — Consult Benchmarks Before Choosing

LLM Model Selection — Consult Benchmarks Before Choosing

When a task requires you to recommend or select an LLM for a specific use case, follow a systematic process before defaulting to a well-known model name:

1. Check Existing Benchmarks First

If you or your team have already run benchmarks (bake-offs) for similar use cases, that prior data is more valuable than generic leaderboards. Reasons:

• Context-specific results trump averages. A model that ranks #2 on MMLU may rank #1 on your specific task (code generation, legal analysis, math reasoning).
• Real-world performance differs from benchmark suites. Generic leaderboards test broad knowledge; your task has specific constraints (latency budget, cost per call, long-context handling, instruction-following precision).
• You avoid redundant work. If you've already benchmarked "agent reasoning on financial data," reuse that result instead of re-running it on a similar financial-domain task.

If an existing benchmark covers your use case: use the winner as your default choice. Note the selection criteria so you can explain the decision.

2. When to Re-Benchmark

If your use case diverges from existing benchmarks, narrow the test scope and run a fresh benchmark:

• Scope mismatch: existing benchmark was for "summarization" but your task is "multi-step reasoning with chain-of-thought"
• Different constraints: existing test was on 4K-token documents; you need 100K-token handling
• New model release: new frontier models are available since the last benchmark
• Cost/latency window changed: you now have stricter latency budget or cost ceiling

When re-benchmarking, use existing benchmarks as prior signals — especially for known weaknesses (e.g., "GPT models historically fail at precise JSON output; Claude handles it better"). This lets you design a test that's smaller and faster than the original.

3. Always Validate Availability & Pricing Live

Never trust model names or pricing from memory or old docs. Models are deprecated, renamed, and repriced frequently. Always query the live API or vendor's current listing:

• Check the model catalog (OpenRouter, Claude API, Together.ai, local LLM registry, etc.) for current slugs and availability.
• Validate pricing — per-1M-token costs change, some models graduate from beta pricing.
• Confirm context window — older docs may list smaller limits than current releases.
• Check availability in your region — some models are geo-gated.

This query takes seconds and prevents selecting a model that no longer exists or is no longer cost-effective for your budget.

4. Generic Leaderboards as a Last Resort

If you have no prior benchmarks and no time to run one, generic leaderboards (Artificial Analysis, Vellum, HELM, MMLU rankings) are a starting point, not the final answer. Use them to identify promising candidates, then ask yourself: