---
language:
- en
license: apache-2.0
task_categories:
- text-generation
- question-answering
tags:
- benchmark
- coding
- math
- science
- reasoning
- logic
- evaluation
pretty_name: CHIMERA Bench v3.0 Mega
size_categories:
- 1K<n<10K
---

# CHIMERA Bench v3.0 Mega

**Comprehensive Hybrid Intelligence Metric for Excellence in Reasoning & Analysis**

**8503 articulated multi-step problems** across 4 domains (larger than GSM8K).

| Domain | Problems | Focus |
|--------|----------|-------|
| **MATH** | 3803 | Multi-step word problems: shopping, speed/distance, geometry, combinatorics, algebra, number theory, calculus |
| **CODE** | 1500 | Code tracing, bug finding, algorithm design, complexity analysis, OOP, recursion |
| **SCIENCE** | 1500 | Physics (projectile, energy, circuits), chemistry (stoichiometry, pH, gas laws), biology (genetics, ecology) |
| **THINK** | 1700 | Constraint satisfaction, knights & knaves, scheduling, deduction, pattern recognition, estimation |
| **Total** | **8503** | |

## Usage

```python
from datasets import load_dataset
ds = load_dataset("vectionlabs/chimera-bench-v1", split="test")
print(f"{len(ds)} problems loaded")
for p in ds.select(range(5)):
    print(f"[{p['domain']}] {p['title']}: {p['prompt'][:80]}...")
```

## Evaluation Types

| Type | Method |
|------|--------|
| `numeric` | Value within tolerance |
| `exact` | Normalized string match |
| `keyword` | Required keywords present |

## Difficulty: 1-5 stars (10-100 points)

## Citation

```bibtex
@misc{chimera-bench-v3-2025,
  title={CHIMERA Bench v3: Comprehensive Hybrid Intelligence Metric for Excellence in Reasoning and Analysis},
  year={2025},
  url={https://huggingface.co/datasets/vectionlabs/chimera-bench-v1}
}
```