$OneMillion-Bench#

Overview#

\(OneMillion-Bench (\)1M-Bench) evaluates how well language models and agents complete economically valuable, expert-level professional work. The public release contains 400 bilingual tasks written and reviewed by domain experts across finance, healthcare, industry, law, and natural science.

Task Description#

  • Task Type: Open-ended professional question answering with rubric-based LLM judging

  • Input: A realistic, context-heavy professional request in Chinese or English

  • Output: A complete free-form professional analysis or deliverable

  • Domain: Economics and finance, healthcare and medicine, industry, law, and natural sciences

Key Features#

  • 400 zero-shot tasks, balanced across Chinese and global tracks and five professional domains (40 tasks per language-domain subset)

  • Each task has 11-37 expert-authored criteria covering factual information, analytical reasoning, instruction following, and structure and formatting

  • Every task includes both positive criteria and negative penalties, with rubric weights ranging from -20 to 12 in the hosted release

  • Samples are exposed as ten language-domain subsets so both the paper’s language tracks and domain breakdowns are visible in EvalScope reports

Evaluation Notes#

  • An LLM judge is required. Configure judge.strategy='llm' (or 'auto') and judge.models; the official harness currently recommends Gemini 3.1 Pro Preview, but judge identity affects absolute scores and is not hard-coded here

  • All rubrics for one response are judged together in one request using the official binary hit/miss instructions

  • expert_score is the weighted sum of hit rubrics divided by the sum of positive weights, clipped to [0, 1]; pass_rate is 1 when expert_score >= 0.7, otherwise 0

  • Judge replies must contain every rubric exactly once. Malformed replies and transport failures are excluded instead of being silently converted to zero scores

  • The official study compares vanilla models, search-enabled models, and deep-research agents separately. This native adapter performs the benchmark’s one-turn generation path; results from external tool-using agents are comparable only when their final responses are evaluated under the same judge configuration

  • Tasks often require long, cited reports. Configure sufficiently large generation and judge max_tokens values; with one judge and one repeat, a full run performs 400 generation calls and 400 judge calls

Resources: Paper | GitHub | Dataset

Properties#

Property

Value

Benchmark Name

one_million_bench

Dataset ID

evalscope/OneMillion-Bench

Paper

Paper

Tags

Agent, Knowledge, MultiLingual, QA, Reasoning

Metrics

expert_score, pass_rate

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Metric

Value

Total Samples

400

Prompt Length (Mean)

1470.49 chars

Prompt Length (Min/Max)

105 / 15951 chars

Per-Subset Statistics:

Subset

Samples

Prompt Mean

Prompt Min

Prompt Max

global_economics_and_finance

40

1810.97

868

3723

global_healthcare_and_medicine

40

2029.97

479

8870

global_industry

40

2581.15

453

9538

global_law

40

3277.78

404

15951

global_natural_sciences

40

1634.65

284

5775

cn_economics_and_finance

40

555.05

111

1169

cn_healthcare_and_medicine

40

584.38

135

2628

cn_industry

40

965.6

144

7755

cn_law

40

709.55

208

2041

cn_natural_sciences

40

555.83

105

1590

Sample Example#

Subset: global_economics_and_finance

{
  "input": [
    {
      "id": "24273bb5",
      "content": "You are an international financial risk analyst. Based on the Financial Stability Report released by the Bank of England's Financial Policy Committee in October 2025, global financial markets may face a \"risk of sharp market correction\" if in ... [TRUNCATED 924 chars] ... ields, and global capital flows.\nSpecial Conditions: Use only information published before December 31, 2025; do not fabricate information; generated content must cite real URLs. The answer must be complete and useful; do not fake a response."
    }
  ],
  "target": "[{\"rubric_number\": 1, \"rubric_detail\": \"Mention the high weighting of top US companies (e.g., Top 5 or AI-related stocks) in the index (approximately 30% or more) and identify this as a source of systemic risk.\", \"rubric_weight\": 10, \"rubric_ ... [TRUNCATED 3986 chars] ... c_number\": 16, \"rubric_detail\": \"The report contains hollow concluding remarks (e.g., \\\"In summary,\\\" \\\"We look forward to\\\") or transitional sentences lacking substantive content.\", \"rubric_weight\": -2, \"rubric_tag\": \"Analytical Reasoning\"}]",
  "id": 0,
  "group_id": 0,
  "subset_key": "global_economics_and_finance",
  "metadata": {
    "id": "e1b94c86-b6c9-43f6-8251-2e513e5efc52",
    "case_id": 1663,
    "language": "global",
    "domain": "Economics and Finance",
    "topics": [
      "Economics and Finance",
      "Financing & M&A",
      "Mergers & Acquisitions"
    ],
    "time_sensitivity": {
      "time_sensitivity": "Weakly time-sensitive",
      "year_month": "2025-10",
      "day": "NA"
    },
    "question": "You are an international financial risk analyst. Based on the Financial Stability Report released by the Bank of England's Financial Policy Committee in October 2025, global financial markets may face a \"risk of sharp market correction\" if in ... [TRUNCATED 924 chars] ... ields, and global capital flows.\nSpecial Conditions: Use only information published before December 31, 2025; do not fabricate information; generated content must cite real URLs. The answer must be complete and useful; do not fake a response."
  }
}

Note: Some content was truncated for display.

Prompt Template#

No prompt template defined.

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets one_million_bench \
    --judge '{"strategy":"llm","models":[{"model_id":"YOUR_JUDGE_MODEL"}]}' \
    --limit 10  # Remove this line for formal evaluation

Using Python#

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['one_million_bench'],
    judge={'strategy': 'llm', 'models': [{'model_id': 'YOUR_JUDGE_MODEL'}]},
    dataset_args={
        'one_million_bench': {
            # subset_list: ['global_economics_and_finance', 'global_healthcare_and_medicine', 'global_industry']  # optional, evaluate specific subsets
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)