--- title: "STT vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/coqui-ai-stt-vs-lm-sys-fastchat" tools: ["coqui-ai-stt", "lm-sys-fastchat"] --- # STT vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick STT when sTT is primarily C++; FastChat is Python; pick FastChat when fastChat is primarily Python; STT is C++. [STT](https://coqui.ai) reports 2.6k GitHub stars, 299 forks, and 106 open issues, last pushed Mar 11, 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 [STT's repository](https://github.com/coqui-ai/STT) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [STT](/tools/coqui-ai-stt.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy. | An open platform for training, serving, and evaluating large language models | | Stars | 2,590 | 39,490 | | Forks | 299 | 4,788 | | Open issues | 106 | 1,027 | | Language | C++ | Python | | 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 | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | Apache-2.0 | | Categories | Model Training, Inference & Serving, Speech & Audio | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [STT](/tools/coqui-ai-stt.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 852d | 71d | | Open issues (now) | 106 | 1.0k | | Full report | [trust report](/tools/coqui-ai-stt/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## 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 STT if… - STT is primarily C++; FastChat is Python. - License: STT is MPL-2.0, FastChat is Apache-2.0. - Tags unique to STT: deep-learning, automatic-speech-recognition, asr, speech-recognition-api. - Also covers Speech & Audio. ### Choose FastChat if… - FastChat is primarily Python; STT is C++. - License: FastChat is Apache-2.0, STT is MPL-2.0. - Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. - Also covers LLM Frameworks, Evaluation & Observability. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use STT - Last GitHub push was 853 days ago (dormant maintenance, Mar 11, 2024). Validate activity before betting a new project on STT. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## 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 STT and FastChat? STT: 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.. 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 STT over FastChat? Choose STT over FastChat when STT is primarily C++; FastChat is Python; License: STT is MPL-2.0, FastChat is Apache-2.0; Tags unique to STT: deep-learning, automatic-speech-recognition, asr, speech-recognition-api; Also covers Speech & Audio. ### When should I choose FastChat over STT? Choose FastChat over STT when FastChat is primarily Python; STT is C++; License: FastChat is Apache-2.0, STT is MPL-2.0; Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers LLM Frameworks, Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid STT? Last GitHub push was 853 days ago (dormant maintenance, Mar 11, 2024). Validate activity before betting a new project on STT. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### 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 STT or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 2,590). Stars measure visibility, not whether either tool fits your constraints. ### Are STT and FastChat open source? Yes - both are open-source projects on GitHub (STT: MPL-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to STT or FastChat? GraphCanon lists graph-backed alternatives at [STT alternatives](/tools/coqui-ai-stt/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([STT markdown twin](/tools/coqui-ai-stt/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/coqui-ai-stt-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, STT or FastChat? STT: 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 STT and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [STT trust report](/tools/coqui-ai-stt/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=coqui-ai-stt`](/api/graphcanon/graph?tool=coqui-ai-stt) - 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/_