Methodology
How we measure community sentiment toward AI coding tools and AI providers.
Data Collection
We collect posts and comments from AI coding communities on Reddit in a weekly batch, capped at the top 50 comments per post. Our historical dataset (mid-2025 onwards) additionally covers 24 general AI subreddits, which powers the long-run trend views.
Tracked Communities
- r/ClaudeAI
- r/ChatGPTCoding
- r/cursor
- r/GithubCopilot
- r/Codeium
- r/vibecoding
- r/ChatGPT
- r/LocalLLaMA
- r/AI_Agents
- r/LLMDevs
The Vibe Check — AI Coding Tool Leaderboard
Every post and comment is checked for opinions about six AI coding tools: Claude Code, Codex CLI, Gemini CLI, Cursor, GitHub Copilot and Windsurf. A single comment can rate several tools at once (e.g. positive about Claude Code, negative about Copilot). Sentiment per tool uses the same 6-point scale described below, and the leaderboard score is the same ±100 weighted score defined in the Scoring section. The leaderboard updates with each weekly batch.
Sentiment Classification
Each piece of content is analyzed by an LLM to classify sentiment toward three major AI providers. We use the arcee-ai/trinity-large-preview model via OpenRouter for fast and cost-efficient classification.
Classification Process
For each post or comment, the LLM analyzes the text and determines the sentiment expressed toward each AI provider. The model considers context, sarcasm, and comparative statements to provide accurate classifications. A single piece of content can express different sentiments toward different providers (e.g., positive about Claude, negative about GPT).
Anthropic (Claude)
Includes Claude 3.5, Claude 4, Claude 4.5 (Opus, Sonnet, Haiku), Claude Code
OpenAI (GPT)
Includes GPT-4o, GPT-5, ChatGPT, o1, o3, o4-mini, Codex
Google (Gemini)
Includes Gemini 1.5, Gemini 2.0, Gemini 3, Gemini Flash, Gemma
Sentiment Scale
Content is classified on a 6-point scale:
- Very Positive Strong enthusiasm, strong recommendation, expressions like "best", "amazing", "love"
- Positive Praise, satisfaction, mild preference, general approval
- Neutral Factual mention, questions, no clear opinion, balanced comparisons
- Negative Criticism, disappointment, mild complaint, frustration
- Very Negative Strong criticism, frustration, rejection, expressions like "worst", "hate", "terrible"
- Not Mentioned Provider not referenced in content
Scoring
The leaderboard score is calculated using weighted sentiment:
Score = (very_positive × 2 + positive × 1 - negative × 1 - very_negative × 2) / total_opinionated × 100 Neutral mentions are excluded from the score calculation but included in total mention counts. This gives extra weight to strong opinions while normalizing across different mention volumes.
Limitations
- Reddit bias: Reddit users don't represent all AI users. The platform skews toward technical, English-speaking users.
- Subreddit bias: Provider-specific subreddits (r/ClaudeAI, r/ChatGPT) naturally have more positive sentiment toward their subject.
- Classification accuracy: Current models achieve ~80% accuracy on our golden test set. Sarcasm and nuanced comparisons are particularly challenging.
- Temporal effects: Major releases, outages, or news events can cause temporary sentiment spikes.
Updates
Data is collected and classified in a weekly batch. Each run collects posts and comments from the past week across the tracked coding communities (top 50 comments per post), then processes the new content through the sentiment classification pipeline.
The dashboard reflects the cumulative sentiment data, with the ability to filter by date range to analyze specific time periods. The "Last Updated" timestamp in the stats bar shows when the most recent data collection occurred.