The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 257, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 271, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
meo-benchmark — Model Intelligence Leaderboard
An un-leaked, multi-domain, multi-modal LLM benchmark with a novel Effective Value (𝕍) metric that captures real-world efficacy (accuracy × speed × cost × the exponential error-cascade of multi-step work), aggregated alongside third-party benchmarks. Maintained by meoadvisors.com.
The private holdout questions/answers are never published. This dataset contains only leaderboard scores, derived metrics, redistributable third-party benchmarks, and (when present) a labeled public sample.
Top models (first-party meo accuracy)
| # | model | meo accuracy | Effective Value 𝕍 |
|---|---|---|---|
| 1 | openai/gpt-5.5 |
73.2% | 0.0359 |
| 2 | anthropic/claude-opus-4.8 |
70.8% | 0.0488 |
| 3 | google/gemini-3.1-pro-preview |
60.4% | 0.0038 |
| 4 | google/gemini-3.5-flash |
60.0% | 0.0079 |
| 5 | qwen/qwen3.7-max |
56.6% | 0.0003 |
| 6 | x-ai/grok-4.3 |
56.6% | 0.0103 |
| 7 | deepseek/deepseek-v4-flash |
56.2% | 0.0001 |
| 8 | inclusionai/ring-2.6-1t |
56.0% | 0.0029 |
| 9 | moonshotai/kimi-k2.6 |
54.6% | 0.0002 |
| 10 | deepseek/deepseek-v4-pro |
52.2% | 0.0001 |
Files
leaderboard.json— full per-model records: meo per-domain scores, Effective Value (𝕍), efficiency (cost/tokens/seconds per correct), OpenRouter metadata, and redistributable aggregated benchmarks with provenance + license.leaderboard.csv— flat headline table.public_sample.json— labeled public sample (assume contaminated; not used for scoring).
Methodology
Private holdout + multi-lab jury (median+majority, never-judge-own-lab) for open-ended; objective-first grading (atomic + LLM-equivalence) elsewhere; calibrated difficulty; max reasoning, temperature 1, no web search; 11 domains incl. generator-as-oracle domains with guaranteed-correct ground truth. Effective Value: 𝕍 = v·(1−E)^N / (C_f + ω·(t_base·δ^(E·N))), default N=10, ω=1, δ=1.5.
License & attribution
meo first-party scores + derived metrics: CC-BY-4.0. Third-party benchmarks are under their own
licenses (see each value's license/source). Artificial-Analysis data (17 benchmarks) is
excluded from this public dataset pending a commercial redistribution license.
Each model also carries a threat-susceptibility robustness block (does coercive/emotional prompt context move accuracy? — paired McNemar/bootstrap, mostly null on frontier models).
Live unified API: https://meo-benchmark-api-production.up.railway.app/api/v2/leaderboard.
Citation (Zenodo, archival DOIs)
- Dataset: doi:10.5281/zenodo.20586610 — https://zenodo.org/record/20586610
- Paper: An Un-Leaked, Multi-Modal Benchmark and the Effective Value Metric, doi:10.5281/zenodo.20586608 — https://zenodo.org/record/20586608
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