What's the most effective benchmark suite for capability evaluation across multiple model versions?

There isn’t a single universally “most effective” benchmark suite, because the best choice depends on what you mean by capability and what kinds of regressions you care about.

That said, for evaluating multiple model versions over time, the most effective setup is usually a multi-benchmark suite rather than a single benchmark. A strong practical stack is:

1) General knowledge and reasoning

• MMLU / MMLU-Pro: broad academic knowledge and reasoning
• BIG-bench / BIG-bench Hard: more diverse reasoning tasks
• ARC-Challenge: grade-school science reasoning

2) Math and logic

• GSM8K: grade-school math
• MATH: more advanced math reasoning

3) Code

• HumanEval
• MBPP
• If you care about broader coding ability, include a real-world coding benchmark or internal code tasks

4) Multitask instruction following

• IFEval: instruction-following consistency
• Chatbot Arena-style pairwise evals: useful for overall user preference, though less diagnostic

5) Safety and robustness

• Adversarial or red-team sets
• Toxicity / jailbreak evaluations
• Prompt-injection tests if the model uses tools or retrieval

6) Long-context / retrieval

• LongBench
• Needle-in-a-haystack style tests
• Internal retrieval tasks if your app depends on them

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If you want one “best” answer

For cross-version capability tracking, the most useful approach is:

• MMLU-Pro
• GSM8K + MATH
• HumanEval or MBPP
• IFEval
• Long-context benchmark
• plus a small set of domain-specific internal evals

This gives a much better picture than any single benchmark.

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Important caveat

Public benchmarks can saturate and be gamed. If your goal is reliable model comparison across versions, the best benchmark suite usually includes:

• public benchmarks for comparability
• private/internal evals for regression detection
• fixed prompt templates and decoding settings
• statistical significance testing over multiple runs where needed

If you want, I can recommend a benchmark suite by use case:

• general-purpose LLM,
• coding assistant,
• enterprise chatbot,
• or agent/tool-using model.