---
title: "Awesome-Code-LLM vs FastChat"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/huybery-awesome-code-llm-vs-lm-sys-fastchat"
tools: ["huybery-awesome-code-llm", "lm-sys-fastchat"]
---

# Awesome-Code-LLM vs FastChat

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers; pick FastChat if fastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB.

[Awesome-Code-LLM](https://github.com/huybery/Awesome-Code-LLM) reports 1.3k GitHub stars, 74 forks, and 3 open issues, last pushed Dec 10, 2024. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [Awesome-Code-LLM's repository](https://github.com/huybery/Awesome-Code-LLM) and [FastChat's repository](https://github.com/lm-sys/FastChat).

| | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [FastChat](/tools/lm-sys-fastchat.md) |
| --- | --- | --- |
| Tagline | 👨💻 An awesome and curated list of best code-LLM for research. | An open platform for training, serving, and evaluating large language models |
| Stars | 1,288 | 39,490 |
| Forks | 74 | 4,788 |
| Open issues | 3 | 1,027 |
| Language | - | Python |
| Adopt for | Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers. | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB |
| Persona | - | - |
| Runtime | - | - |
| License | MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions. | Apache-2.0 |
| Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training |

## Trust and health

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

| | [Awesome-Code-LLM](/tools/huybery-awesome-code-llm.md) | [FastChat](/tools/lm-sys-fastchat.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Steady (60%) |
| Days since push | 578d | 71d |
| Open issues (now) | 3 | 1.0k |
| Owner type | User | Organization |
| Full report | [trust report](/tools/huybery-awesome-code-llm/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) |

## Decision facts: Awesome-Code-LLM

- **Requirements:** No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.
- **Adopt for:** Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers.
- **License detail:** MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions.

## Decision facts: FastChat

- **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB

## Choose when

### Choose Awesome-Code-LLM if…

- License: Awesome-Code-LLM is MIT, FastChat is Apache-2.0.
- Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs..
- Tags unique to Awesome-Code-LLM: awesome, code-generation.
- When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval.

### Choose FastChat if…

- License: FastChat is Apache-2.0, Awesome-Code-LLM is MIT.
- Tags unique to FastChat: chatbots, distributed serving, evaluation system.
- Also covers Inference & Serving, Model Training.
- - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.

## When NOT to use Awesome-Code-LLM

- When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision.
- If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality.
- In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering

## When NOT to use FastChat

- - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions.
- - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations).
- - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on
- + Mac.

## Common questions

### What is the difference between Awesome-Code-LLM and FastChat?

Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Code-LLM over FastChat?

Choose Awesome-Code-LLM over FastChat when License: Awesome-Code-LLM is MIT, FastChat is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, code-generation; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval.

### When should I choose FastChat over Awesome-Code-LLM?

Choose FastChat over Awesome-Code-LLM when License: FastChat is Apache-2.0, Awesome-Code-LLM is MIT; Tags unique to FastChat: chatbots, distributed serving, evaluation system; Also covers Inference & Serving, Model Training; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.

### When should I avoid Awesome-Code-LLM?

When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering

### When should I avoid FastChat?

- You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac.

### Is Awesome-Code-LLM or FastChat more popular on GitHub?

FastChat has more GitHub stars (39,490 vs 1,288). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Code-LLM and FastChat open source?

Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, FastChat: Apache-2.0).

### Where can I find alternatives to Awesome-Code-LLM or FastChat?

GraphCanon lists graph-backed alternatives at [Awesome-Code-LLM alternatives](/tools/huybery-awesome-code-llm/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([Awesome-Code-LLM markdown twin](/tools/huybery-awesome-code-llm/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/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/huybery-awesome-code-llm-vs-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-Code-LLM or FastChat?

Awesome-Code-LLM: Dormant. FastChat: Steady. 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 Awesome-Code-LLM and FastChat?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Code-LLM trust report](/tools/huybery-awesome-code-llm/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=huybery-awesome-code-llm`](/api/graphcanon/graph?tool=huybery-awesome-code-llm)
- 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/_