---
license: cc-by-nc-4.0
task_categories:
- question-answering
- multiple-choice
language:
- en
tags:
- jee
- neet
- stem
- education
- india
- physics
- chemistry
- mathematics
- biology
- benchmark
- leaderboard
- exam
- competitive-exam
- fine-tuning
- evaluation
- rlvr
- verifiable-rewards
size_categories:
- 1K<n<10K
pretty_name: 'NalandaJEENEETBench: JEE & NEET STEM Benchmark'
extra_gated_heading: Request access to Nalandadata
extra_gated_prompt: >-
  Access requires manual review. Please tell us who you are and how you intend
  to use this resource so we can support your work and share licensing options.
  By requesting access you agree not to redistribute the data and to contact
  info@nalandadata.ai for commercial licensing.
extra_gated_fields:
  Name: text
  Organization: text
  Work email: text
  Role: text
  Intended use:
    type: select
    options:
      - Research
      - Model training / post-training
      - Evaluation / benchmarking
      - Commercial product
      - Other
  I agree to contact Nalandadata for commercial licensing: checkbox
extra_gated_button_content: Request access
---

# NalandaJEENEETBench: JEE & NEET STEM Evaluation Benchmark

**The first open benchmark for evaluating LLMs on Indian competitive exam questions (JEE Mains, JEE Advanced, NEET UG).**

NalandaJEENEETBench is a curated sample from [Nalanda Data's](https://nalandadata.ai) proprietary dataset of **116,000+ expert-curated JEE and NEET examination questions** with verified correct answers and step-by-step solutions.

> **📦 Public sample (no login required):** [Nalandadata/NalandaJEENEETBench-sample](https://huggingface.co/datasets/Nalandadata/NalandaJEENEETBench-sample) — 10 benchmark questions, free, no sign-up required.
>
> **🔬 Fine-tuned model:** [Nalandadata/nalanda-qwen-7b-grpo](https://huggingface.co/Nalandadata/nalanda-qwen-7b-grpo) — Qwen 2.5 7B fine-tuned on the full 116K dataset.
>
> **Live demo:** [Nalanda Live Demo](https://huggingface.co/spaces/Nalandadata/nalanda-live-demos) — test JEE & NEET questions against our fine-tuned models.

---

## What's Inside

| Split | Questions | Content | Purpose |
|-------|:---------:|---------|---------|
| `benchmark` | **785** | MCQs with correct answers (no solutions) | **Evaluate** your model on JEE/NEET |
| `train_sample` | **500** | MCQs with correct answers + full step-by-step solutions | **Preview** our training data quality |

Both splits cover 5 subjects: **Physics, Chemistry, Mathematics, Biology, and English** (reading comprehension). Subject distribution reflects the natural mix of JEE/NEET question pools rather than enforced per-subject quotas.

## Why This Benchmark Matters

There is no standard evaluation suite for Indian competitive exams. MMLU covers Western curricula. NalandaJEENEETBench fills this gap:

- **785 held-out MCQs** across the full JEE/NEET syllabus
- **Verified correct answers** — every question has been expert-validated
- **LaTeX math notation** — proper representation of equations and formulas
- **Multi-subject coverage** — Physics, Chemistry, Mathematics, and Biology (each 150+ questions), plus a small English reading-comprehension slice

## Proven Results

> **Note (2026-06-01):** The accuracy figures in the table below were computed
> against an earlier version of this benchmark (800 rows across 4 subjects,
> before content-label corrections). The current 785-row, 5-subject benchmark
> (Physics 167 · Chemistry 167 · Mathematics 207 · Biology 223 · English 21)
> may yield different per-subject numbers. Re-evaluation against the corrected
> split is planned for a future release.

We used the full 116K dataset (not this sample) to fine-tune [Qwen 2.5 7B](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using a two-stage GRPO pipeline.

### Results on NalandaJEENEETBench (this benchmark)

| Subject | Qwen 7B Baseline | Llama 70B Baseline | **+ Our Data (7B)** | Improvement |
|---------|:-----------------:|:------------------:|:-------------------:|:-----------:|
| Physics | 51.0% | 59.0% | **65.0%** | **+14.0pp** |
| Chemistry | 61.5% | 70.0% | **71.5%** | **+10.0pp** |
| Mathematics | 56.0% | 57.5% | **64.5%** | **+8.5pp** |
| Biology | 73.5% | 66.5% | **77.5%** | **+4.0pp** |
| **Overall** | **60.5%** | **63.2%** | **69.6%** | **+9.1pp** |

**A 7B model fine-tuned on our data outperforms a 70B model across all STEM subjects.** The data is the differentiator.

## Quick Start

### Evaluate your model

```python
from datasets import load_dataset

# Load the benchmark
bench = load_dataset("Nalandadata/NalandaJEENEETBench", split="benchmark")

for q in bench:
    prompt = (
        f"Question: {q['question']}\n\n"
        f"(A) {q['option_a']}\n"
        f"(B) {q['option_b']}\n"
        f"(C) {q['option_c']}\n"
        f"(D) {q['option_d']}\n\n"
        f"Think step by step. State your final answer as A, B, C, or D."
    )
    # Run your model on `prompt`
    # Compare output to q['correct_answer']
```

### Preview training data quality

```python
# Load the training sample (includes step-by-step solutions)
train = load_dataset("Nalandadata/NalandaJEENEETBench", split="train_sample")

for q in train.select(range(5)):
    print(f"[{q['subject']}] {q['question'][:100]}...")
    print(f"Answer: ({q['correct_answer']})")
    print(f"Solution: {q['solution'][:200]}...")
    print("---")
```

## Data Schema

### `benchmark` split

| Column | Type | Description |
|--------|------|-------------|
| `subject` | string | Physics, Chemistry, Mathematics, Biology, or English |
| `question` | string | Question text (may contain LaTeX) |
| `option_a` | string | Option (A) |
| `option_b` | string | Option (B) |
| `option_c` | string | Option (C) |
| `option_d` | string | Option (D) |
| `correct_answer` | string | Correct option letter (A, B, C, or D) |

### `train_sample` split

Same as `benchmark`, plus:

| Column | Type | Description |
|--------|------|-------------|
| `solution` | string | Full step-by-step solution with reasoning |

## The Full Dataset

**This is a sample.** The full Nalanda Data question bank contains:

| Property | Value |
|----------|-------|
| **Total questions** | **116,000+** |
| **Physics** | 27,800+ |
| **Chemistry** | 52,500+ |
| **Mathematics** | 25,700+ |
| **Biology** | 9,900+ |
| **Format** | MCQ with verified correct answers + step-by-step solutions |
| **Notation** | LaTeX mathematical notation throughout |
| **Sources** | JEE Mains, JEE Advanced, NEET UG, Board Exams |
| **Quality** | Expert-curated, subject-label verified, deduplicated |

### What makes the full dataset valuable for AI training

1. **Verified correct answers** — enables reinforcement learning (GRPO/DPO/PPO), not just SFT
2. **Step-by-step solutions** — teaches reasoning chains, not just answer selection
3. **LaTeX math notation** — proper STEM representation that models can learn from
4. **Proven results** — demonstrated +9.1pp improvement on a 7B model, outperforming 70B baselines
5. **Competitive moat** — proprietary data that cannot be scraped or replicated

### Licensing

The full dataset is available for licensing. Use cases include:

- **Foundation model pre-training** — enrich STEM reasoning in base models
- **Supervised fine-tuning** — domain adaptation for education and tutoring
- **Reinforcement learning** — GRPO/DPO with verified correct answers as reward signal
- **Evaluation** — benchmark your models on Indian competitive exam performance
- **EdTech products** — power AI tutoring, question generation, and adaptive learning

### Contact

**For commercial licensing, full dataset access, custom data work, or partnerships:**
📧 [info@nalandadata.ai](mailto:info@nalandadata.ai)

**For technical questions, integration help, or fine-tuning support:**
📧 [tech@nalandadata.ai](mailto:tech@nalandadata.ai)

🌐 [nalandadata.ai](https://nalandadata.ai)

---

## Citation

If you use NalandaBench in your research, please cite:

```bibtex
@misc{nalandabench2026,
  title={NalandaJEENEETBench: A JEE and NEET STEM Evaluation Benchmark for Large Language Models},
  author={Nalanda Data},
  year={2026},
  url={https://huggingface.co/datasets/Nalandadata/NalandaJEENEETBench},
  note={Sample from 116K+ expert-curated Indian competitive exam questions. Part of the NalandaBench suite.}
}
```

## Related

- **Fine-tuned model**: [Nalandadata/nalanda-qwen-7b-grpo](https://huggingface.co/Nalandadata/nalanda-qwen-7b-grpo) — Qwen 2.5 7B fine-tuned on the full dataset
- **Paper**: Coming soon — "Domain Data Drives LLM Fine-Tuning Performance: Lessons from 116K JEE/NEET Questions"

## License

This sample dataset is released under **CC BY-NC 4.0** (Creative Commons Attribution-NonCommercial). You may use it for research and evaluation. Commercial use of the full dataset requires a license from Nalanda Data.