Introduction

  • TL;DR: Open LLM Leaderboard v2 shifts evaluation toward instruction-following, hard reasoning, long-context multi-step reasoning, and difficult science QA.
  • In the public v2 “contents” view, the Average ranges from 0.74 to ~52.1, and GPQA / MuSR are clear bottlenecks (their maxima are much lower than other tasks).
  • Top entries often include merged/community-tuned models, so you should separate “leaderboard performance” from “production-ready choice.”

Why it matters: If you treat a leaderboard rank as a production verdict, you’ll pick the wrong model.

What Open LLM Leaderboard v2 measures

The six benchmarks (and what they signal)

Hugging Face documents the intent of each benchmark in v2.

  • IFEval: objectively verifiable instruction following.
  • BBH: a hard subset of BIG-bench tasks for challenging reasoning.
  • MATH Level 5: hardest subset of MATH; output formatting matters.
  • GPQA: graduate-level “Google-proof” science QA; access is gated to reduce contamination.
  • MuSR: multi-step reasoning with long problem statements (~1,000 words).
  • MMLU-Pro: harder, cleaner variant of MMLU (10 choices; reasoning-focused).

Why it matters: v2 scores are driven by reasoning + format compliance, not just trivia knowledge.

Shot settings (common v2 setup)

A widely used v2 setup is: BBH 3-shot, GPQA 0-shot, MMLU-Pro 5-shot, MuSR 0-shot, IFEval 0-shot, MATH L5 4-shot.

Why it matters: small differences in templates/shots can move ranks—don’t compare apples to oranges.

Score ceiling is ~52, not 80–90

In the public v2 “contents” viewer, Average spans 0.74 → ~52.1.

Why it matters: at the top end, tiny deltas can represent a few questions, not a real-world leap.

GPQA / MuSR look like the bottleneck axes

Maxima in the same view:

  • IFEval max 90, BBH max 76.7, MATH L5 max 71.5, MMLU-Pro max 70
  • GPQA max 29.4, MuSR max 38.7

Why it matters: if your product resembles “hard QA” or “long-context multi-step reasoning,” focus on GPQA/MuSR, not just the overall average.

Merges/community tuning show up heavily at the top

Example entry visible in the viewer:

  • prithivMLmods/Galactic-Qwen-14B-Exp2 (Qwen2ForCausalLM, ~14.766B params) shows strong headline numbers and is labeled “(Merge)” in the entry.

Why it matters: for production, add filters for reproducibility, licensing, hub availability, and “official” provenance.

When to use the leaderboard (and when not to)

  • Use it for: shortlist compression, regression checks, evaluation design inspiration.
  • Don’t use it for: latency/cost decisions, safety/alignment guarantees, domain RAG quality, multilingual (e.g., Korean) quality.

Why it matters: leaderboard rank is a signal, not a deployment decision.

Troubleshooting

“My math score is oddly low”

Formatting/template mismatches can tank MATH scores even when reasoning is correct; this has been observed in evaluation notes.

“I can’t run GPQA”

GPQA access is gated by design.

“Results are unstable”

Control seed, decoding params, templates, and precision; the dataset tracks precision/template metadata.

Why it matters: most “weird results” are evaluation-condition bugs.

Conclusion

  • v2 shifts evaluation toward reasoning + format compliance + long-context.
  • Overall Average tops out around ~52, and GPQA/MuSR are the harsh bottlenecks.
  • Top ranks often include merged/community models, so production choices need extra filters (license, reproducibility, provenance).

Summary

  • v2 benchmarks: IFEval, BBH, MATH L5, GPQA, MuSR, MMLU-Pro.
  • Average ceiling ~52; GPQA/MuSR are the tightest bottlenecks.
  • Treat the leaderboard as a shortlist tool, not a production verdict.

#openllmleaderboard #huggingface #llmevaluation #benchmarks #ifeval #mmlu_pro #gpqa #musr #bbh #mathbenchmark

References

  • (Open LLM Leaderboard v2 Benchmarks - Hugging Face Docs, 2026-02-08 accessed)[https://huggingface.co/docs/leaderboards/en/open_llm_leaderboard/about]
  • (Open LLM Leaderboard v2 public results table - Hugging Face Datasets, 2026-02-08 accessed)[https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train]
  • (Example top entry merged/community model - Hugging Face Viewer, 2026-02-08 accessed)[https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FGauss-Opus-14B-R999]
  • (IFEval paper - arXiv, 2023-11-14)[https://arxiv.org/abs/2311.07911]
  • (MMLU-Pro paper - arXiv, 2024-06-03)[https://arxiv.org/abs/2406.01574]
  • (MMLU-Pro proceedings record - ACM DL, 2024-12-10)[https://dl.acm.org/doi/10.5555/3737916.3740934]
  • (Practical v2 eval setup - Oumi Docs, 2026-02-08 accessed)[https://oumi.ai/docs/en/latest/user_guides/evaluate/leaderboards.html]
  • (Evaluation guidebook template/output format notes - Hugging Face, 2025-11-27)[https://huggingface.co/spaces/OpenEvals/evaluation-guidebook/commit/49f71ca30a02ae24531cc419c4090e8b88a6530f]