FullStackBench
Multilingual benchmark for evaluating LLMs in full-stack coding
GraphCanon updated 2w · GitHub synced 2w
Decision brief
FullStackBench is a benchmark tool to evaluate large language models in full-stack coding across 16 languages, using 3K test samples.
Good fit when
- When you need to assess LLM performance in full-stack programming tasks covering multiple domains and languages
- If your evaluation requires real-world code development scenarios that span desktop and web applications
Avoid when
- Avoid if testing scope is limited to a single or few programming languages as FullStackBench covers a wide range of languages
- Not suitable if your focus is solely on theoretical coding challenges instead of practical, full-stack tasks
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (455d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No criticals
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for ByteDance. Display-only - separate from trust and ranking.
- Company
- Bytedance Inc.·GitHub org profile·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install FullStackBench PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
FullStack Bench is a multilingual benchmark designed to assess the capabilities of large language models (LLMs) in real-world full-stack programming tasks across 16 languages with 3K test samples.
Capability facts
- Languages
- python
Source: github.language · Aug 5, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 5, 2026)
<a href="https://www.python.org/">Source link
Tags
README
FullStack Bench: Evaluating LLMs as Full Stack Coders
🏠 FullStack Bench Code • 📊 Benchmark Data • 📚 SandboxFusion
Table of contents
- FullStack Bench: Evaluating LLMs as Full Stack Coders
- 📌 Introduction
- 📚 SandboxFusion
- 📊 Data
- 💻 Usage
- 📖 Citation
📌Introduction
FullStack Bench is a multilingual benchmark for full-stack programming, covering a wide range of application domains and 16 programming languages with 3K test samples, which substantially pushes the limits of code LLMs in code-related abilities of the real-world code development scenarios.
Task Examples
FullStack Bench covers more mainstream application domains when compared to existing code evaluation benchmarks. Here is a visualization example from FullStack Bench, where the model is tasked with solving problems in the domain of desktop and web development using HTML.
Refer to our paper or dataset for more details.
Results
Refer to our paper for more results.
📚SandboxFusion
SandboxFusion is an an effective code sandbox execution tool to evaluate different programming tasks from different languages. It incorporates over 10 coding-related evaluation datasets, featuring a standardized data format and accessible via a uniform HTTP API.
Refer to our paper and 📚 Tutorial for more Details.
📊Data
| Dataset | Download |
|---|---|
| FullStack Bench Dataset | 🤗 HuggingFace |
💻Usage
Start the sandbox server:
docker run -d --rm -p 8080:8080 volcengine/sandbox-fusion:server-20241204
For users in mainland China, the following mirror is provided:
docker run -d --rm -p 8080:8080 vemlp-cn-beijing.cr.volces.com/preset-images/code-sandbox:server-20241204
Then, run the benchmark:
git clone https://github.com/bytedance/FullStackBench.git
cd FullStackBench
pip install -r requirements.txt
# modify the model configs in src/main.py
python src/main.py
📖Citation
If you find our work helpful, please use the following citations.
@misc{liu2024fullstackbenchevaluatingllms,
title={FullStack Bench: Evaluating LLMs as Full Stack Coders},
author={Siyao Liu and He Zhu and Jerry Liu and Shulin Xin and Aoyan Li and Rui Long and Li Chen and Jack Yang
For agents
This page has a .md twin and JSON over the API.