Benchmark-focused pages
Task pages surface state-of-the-art results for a benchmark such as MMLU, letting readers see which papers are associated with a given evaluation target.
Papers with Code
Papers with Code is a research discovery site for browsing machine learning benchmarks, trending papers, and linked implementations. It helps readers track state-of-the-art results and move from a paper summary to arXiv or GitHub when available.
Overview
Papers with Code is a research discovery site that organizes machine learning papers around tasks, benchmarks, and code implementations. On the MMLU state-of-the-art page, it presents a task-specific view that helps readers see which papers are associated with a given benchmark and how those papers are connected to public research artifacts.
The collected pages also show a trending-papers view, where entries include paper titles, short summaries, publication dates, author counts or source names, community upvotes, and links to arXiv and GitHub when available. That combination makes the site useful for scanning new research, following benchmark activity, and jumping from a paper summary to the underlying paper or code.
What Papers with Code provides
Trending paper feed
Paper listings show the paper title, publication date, author count or source, and short summary so readers can scan recent research quickly.
Paper and code links
Entries include links to arXiv pages and GitHub repositories when available, connecting results to the paper text and implementation.
Time-based browsing
Results can be browsed in daily, weekly, and monthly views, helping readers switch between short-term and longer-horizon trends.
Community signals
The pages present upvote counts and submission attribution, which helps identify what the community is engaging with on the site.
Comparison-oriented layout
The benchmark and paper pages give a comparison-oriented view of research outputs, making it easier to track how methods relate to one another on the same task.
Common ways people use Papers with Code
Check benchmark leaders
Use the MMLU state-of-the-art page to identify which papers are associated with the benchmark and compare research progress on a single task.
Monitor new research
Open the trending papers feed to scan recent papers, read short summaries, and decide which papers deserve a deeper look.
Follow paper-to-code links
Move from a paper entry to the linked arXiv page or GitHub repository to inspect the full paper or implementation details.
Track momentum over time
Browse daily, weekly, or monthly views to understand what is gaining attention over different time windows.
Pros and Cons
Pros
- Organizes research by benchmark and task, which makes state-of-the-art tracking straightforward.
- Connects paper summaries to arXiv pages and GitHub repositories when available.
- Provides a quick way to scan trending papers with publication dates, author/source info, and short descriptions.
- Offers multiple browsing views such as daily, weekly, and monthly trending lists.
Cons
- The collected pages do not show full pricing, account, or team-workflow details.
- Coverage appears uneven across papers and benchmarks, since not every entry includes the same metadata or linked resources.
FAQ
Do I need an account to browse the site?
Papers with Code organizes paper pages and benchmark pages around tasks and leaderboards, so it is used for exploring results rather than setting up software. The source pages do not describe an onboarding flow or account requirement for browsing public content.
Is it designed for individual use or team use?
The collected pages show trending papers, paper detail pages, and benchmark/task pages. They do not document a team workflow, collaboration features, or a shared workspace on these pages.
How much does it cost?
The provided pages do not expose a formal pricing table for this content. Based on the collected source text, no paid plan details or price points are shown on the pages reviewed.
What outputs can I expect from a paper or benchmark page?
The site links papers to arXiv pages and GitHub repositories where available, which helps readers move from a benchmark result to the underlying paper and code. The exact level of coverage depends on the specific paper or benchmark entry.
Quick Facts
- Category
- Research discovery
- Primary use
- Tracking state-of-the-art results and trending papers
- Source domain
- paperswithcode.com
- Content types
- Benchmarks, paper pages, trending lists
- Linked resources
- arXiv and GitHub when available
- Browsing modes
- Daily, weekly, monthly
Alternativas a Papers with Code
蓝藻AI
蓝藻AI是一款在线AI配音与语音合成产品，可将文字转成语音，并支持自助声音克隆。页面信息显示它面向短视频、有声书等需要配音的内容场景。
Noiz AI
Noiz AI is an AI text-to-speech, voice cloning, and voice design tool for creating lifelike speech from text. It also lets users shape voice delivery, including emotion, within the same workflow.
Gemini 3.1 Flash TTS
Gemini 3.1 Flash TTS is Google’s preview text-to-speech model for generating expressive AI speech with fine-grained control over style and delivery. It is available across the Gemini API, Google AI Studio, Vertex AI, and Google Vids.
Ondoku
Ondoku 是一款基于浏览器的文字转语音软件，可将文本转换为可下载的 .mp3 语音，并提供免费额度与付费方案。它支持多语言朗读、图片朗读以及按规则商用。
Typecast
Typecast is an online AI voice generator that turns text into life-like speech with emotional delivery and a selection of hyper-realistic voices. It is a browser-based tool for creating spoken audio from written content.
魔音工坊 (Moying Gongfang)
魔音工坊 (Moying Gongfang) es una plataforma inteligente de texto a voz (TTS) en línea que convierte texto escrito en locuciones de alta calidad utilizando voces humanas realistas con diversos acentos.