# The Multivac — Evaluation Report

**Evaluation ID:** EVAL-20260207-143651
**Date:** Jan 21, 2026
**Category:** analysis
**Question ID:** ANALYSIS-002

---

## Question

You receive this dataset summary for a machine learning project predicting customer churn:

```
Total records: 50,000
Features: 45
Target: churned (1) vs retained (0)
Class balance: 5% churned, 95% retained

Sample statistics:
- age: mean=34.2, std=12.1, min=-5, max=150
- account_balance: mean=$2,340, std=$15,000, 5000 missing values
- last_login: 30% missing, format varies (MM/DD/YYYY and YYYY-MM-DD mixed)
- customer_id: unique count = 48,500 (out of 50,000 records)
- country: 200 unique values including "USA", "usa", "United States", "US"

Correlation with target:
- age: 0.02
- account_balance: 0.45
- days_since_last_login: 0.67
- total_purchases: 0.52
```

What data quality issues do you identify? What would you do before training a model?

---

## Winner

**MiMo-V2-Flash** (Xiaomi)
- Winner Score: 9.77
- Matrix Average: 9.48
- Total Judgments: 90

---

## Rankings

| Rank | Model | Provider | Avg Score | Judgments |
|------|-------|----------|-----------|----------|
| 1 | MiMo-V2-Flash | Xiaomi | 9.77 | 8 |
| 2 | Gemini 3 Flash Preview | Google | 9.67 | 7 |
| 3 | Claude Sonnet 4.5 | Anthropic | 9.63 | 8 |
| 4 | GPT-OSS-Legal | OpenAI | 9.59 | 9 |
| 5 | DeepSeek V3.2 | DeepSeek | 9.56 | 7 |
| 6 | Grok 4.1 Fast | xAI | 9.54 | 7 |
| 7 | GPT-OSS-120B | OpenAI | 9.51 | 8 |
| 8 | Gemini 2.5 Flash | Google | 9.50 | 6 |
| 9 | Claude Opus 4.5 | Anthropic | 9.34 | 7 |
| 10 | Gemini 3 Pro Preview | Google | 8.68 | 8 |

---

## 10×10 Judgment Matrix

Rows = Judge, Columns = Respondent. Self-judgments excluded (—).

| Judge ↓ / Resp → | MiMo-V2-Flash | GPT-OSS-Legal | Gemini 2.5 | Gemini 3 | GPT-OSS-120B | DeepSeek V3.2 | Claude Sonnet | Claude Opus | Gemini 3 | Grok 4.1 Fast |
|---|---|---|---|---|---|---|---|---|---|---|
| MiMo-V2-Flash | — | 9.0 | 8.8 | 9.0 | 9.3 | 9.0 | 9.6 | 9.0 | 8.6 | 9.0 |
| GPT-OSS-Legal | 0.0 | — | 0.0 | 0.0 | 9.0 | 0.0 | 9.3 | 8.6 | 7.5 | 0.0 |
| Gemini 2.5 | 10.0 | 10.0 | — | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 9.0 | 10.0 |
| Gemini 3 | 9.8 | 9.8 | 9.8 | — | 9.8 | 9.8 | 9.8 | 9.8 | 9.6 | 9.8 |
| GPT-OSS-120B | 9.4 | 9.0 | 0.0 | 0.0 | — | 9.0 | 9.3 | 8.8 | 7.2 | 9.0 |
| DeepSeek V3.2 | 10.0 | 9.8 | 9.8 | 9.3 | 9.6 | — | 9.6 | 0.0 | 0.0 | 10.0 |
| Claude Sonnet | 9.6 | 9.6 | 9.8 | 9.6 | 9.6 | 9.6 | — | 9.3 | 9.0 | 9.3 |
| Claude Opus | 9.6 | 9.6 | 9.0 | 9.8 | 9.0 | 9.6 | 9.8 | — | 9.0 | 9.6 |
| Gemini 3 | 10.0 | 9.8 | 0.0 | 10.0 | 0.0 | 0.0 | 0.0 | 0.0 | — | 0.0 |
| Grok 4.1 Fast | 9.8 | 9.8 | 9.8 | 10.0 | 9.8 | 10.0 | 9.8 | 9.8 | 9.6 | — |

---

## Methodology

- **10×10 Blind Peer Matrix:** All models answer the same question, then all models judge all responses.
- **5 Criteria:** Correctness, completeness, clarity, depth, usefulness (each scored 1–10).
- **Self-judgments excluded:** Models do not judge their own responses.
- **Weighted Score:** Composite of all 5 criteria.

---

## Citation

The Multivac (2026). Blind Peer Evaluation: ANALYSIS-002. app.themultivac.com

## License

Open data. Free to use, share, and build upon. Please cite The Multivac when using this data.

Download raw JSON: https://app.themultivac.com/api/evaluations/EVAL-20260207-143651/results
Full dataset: https://app.themultivac.com/dashboard/export