Interactive price comparison for AI models (LLMs and Speech-to-Text)

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Demo: https://ai-pricing.vercel.app/ GitHub: https://github.com/WiegerWolf/ai-pricing I built this tool to solve a common problem: understanding the real cost of using different AI models at scale. It compares pricing and performance metrics across major AI providers, with interactive calculators for both Large Language Models and Speech-to-Text services. What it does: - Compares 20+ LLMs (GPT-4, Claude 3, Gemini 1.5, Groq, etc.) - Shows Arena ELO scores, processing speeds, context sizes - Calculates monthly costs based on token usage - Compares STT services (Deepgram, AssemblyAI, OpenAI Whisper, etc.) - Displays per-minute/hour pricing and free quotas - Updates regularly with latest models and pricing Technical details: - No frameworks or dependencies, just vanilla HTML/CSS/JS - Data stored in JSON files for easy updates - Responsive design, works on mobile - Keyboard navigation support - Open source, MIT licensed Example use cases: - Calculate costs for different usage scenarios - Compare model performance vs price - Find models with free tiers - Check real-time STT capabilities - Verify context window limitations The tool helped me choose providers for several projects, so I thought others might find it useful too. Feedback welcome!

Visit product View on Hacker NewsOct 28, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios, gemini · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios, month, monthly · Missing: mobile apps, personal, entrepreneurs

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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