---
title: "Made-With-ML vs Open-LLM-Leaderboard"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-vila-lab-open-llm-leaderboard"
tools: ["gokumohandas-made-with-ml", "vila-lab-open-llm-leaderboard"]
---

# Made-With-ML vs Open-LLM-Leaderboard

*GraphCanon updated Jul 15, 2026*

## Verdict

Pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; Open-LLM-Leaderboard is Python; pick Open-LLM-Leaderboard when open-LLM-Leaderboard is primarily Python; Made-With-ML is Jupyter Notebook.

[Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [Open-LLM-Leaderboard](https://huggingface.co/spaces/Open-Style/OSQ-Leaderboard) has 53 stars, 7 forks, and 1 open issues, last pushed Jun 27, 2024. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [Open-LLM-Leaderboard's repository](https://github.com/VILA-Lab/Open-LLM-Leaderboard).

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) |
| --- | --- | --- |
| Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | Open-LLM-Leaderboard: Open-Style Question Evaluation. Paper at https://arxiv.org/abs/2406.07545 |
| Stars | 48,703 | 53 |
| Forks | 7,661 | 7 |
| Open issues | 27 | 1 |
| Language | Jupyter Notebook | Python |
| Adopt for | - | - |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | CC-BY-4.0 |
| Categories | AI Agents, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks, Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) |
| --- | --- | --- |
| Maintenance | Slowing (36%) | Dormant (18%) |
| Days since push | 132d | 747d |
| Open issues (now) | 27 | 1 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/vila-lab-open-llm-leaderboard/trust.md) |

## Shared compatibility

- **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) - Python runtime

## Choose when

### Choose Made-With-ML if…

- Made-With-ML is primarily Jupyter Notebook; Open-LLM-Leaderboard is Python.
- License: Made-With-ML is MIT, Open-LLM-Leaderboard is CC-BY-4.0.
- Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
- Also covers AI Agents.

### Choose Open-LLM-Leaderboard if…

- Open-LLM-Leaderboard is primarily Python; Made-With-ML is Jupyter Notebook.
- License: Open-LLM-Leaderboard is CC-BY-4.0, Made-With-ML is MIT.
- Tags unique to Open-LLM-Leaderboard: leaderboard, llm-evaluation, llm-leaderboard, open-ended-evaluation.
- Also covers Evaluation & Observability.

## When NOT to use Made-With-ML

- Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## When NOT to use Open-LLM-Leaderboard

- Last GitHub push was 748 days ago (dormant maintenance, Jun 27, 2024). Validate activity before betting a new project on Open-LLM-Leaderboard.
- Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

## Common questions

### What is the difference between Made-With-ML and Open-LLM-Leaderboard?

Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. Open-LLM-Leaderboard: Open-LLM-Leaderboard: Open-Style Question Evaluation. Paper at https://arxiv.org/abs/2406.07545. See the comparison table for live GitHub stats and shared categories.

### When should I choose Made-With-ML over Open-LLM-Leaderboard?

Choose Made-With-ML over Open-LLM-Leaderboard when Made-With-ML is primarily Jupyter Notebook; Open-LLM-Leaderboard is Python; License: Made-With-ML is MIT, Open-LLM-Leaderboard is CC-BY-4.0; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents.

### When should I choose Open-LLM-Leaderboard over Made-With-ML?

Choose Open-LLM-Leaderboard over Made-With-ML when Open-LLM-Leaderboard is primarily Python; Made-With-ML is Jupyter Notebook; License: Open-LLM-Leaderboard is CC-BY-4.0, Made-With-ML is MIT; Tags unique to Open-LLM-Leaderboard: leaderboard, llm-evaluation, llm-leaderboard, open-ended-evaluation; Also covers Evaluation & Observability.

### When should I avoid Made-With-ML?

Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### When should I avoid Open-LLM-Leaderboard?

Last GitHub push was 748 days ago (dormant maintenance, Jun 27, 2024). Validate activity before betting a new project on Open-LLM-Leaderboard. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

### Is Made-With-ML or Open-LLM-Leaderboard more popular on GitHub?

Made-With-ML has more GitHub stars (48,703 vs 53). Stars measure visibility, not whether either tool fits your constraints.

### Are Made-With-ML and Open-LLM-Leaderboard open source?

Yes - both are open-source projects on GitHub (Made-With-ML: MIT, Open-LLM-Leaderboard: CC-BY-4.0).

### Where can I find alternatives to Made-With-ML or Open-LLM-Leaderboard?

GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [Open-LLM-Leaderboard alternatives](/tools/vila-lab-open-llm-leaderboard/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [Open-LLM-Leaderboard markdown twin](/tools/vila-lab-open-llm-leaderboard/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/gokumohandas-made-with-ml-vs-vila-lab-open-llm-leaderboard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Made-With-ML or Open-LLM-Leaderboard?

Made-With-ML: Slowing. Open-LLM-Leaderboard: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for Made-With-ML and Open-LLM-Leaderboard?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [Open-LLM-Leaderboard trust report](/tools/vila-lab-open-llm-leaderboard/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_