PromptDiff
Compare LLMs across models. One API call.
What it does
Stop copy-pasting prompts across ChatGPT, Claude, and Gemini. PromptDiff compares LLM outputs in one API call. Send a prompt + pick models. Get back: output, latency (ms), tokens, and cost (USD) per model. 8 models, 4 providers: - Claude Sonnet & Haiku - GPT-4o & 4o-mini - Gemini Pro & Flash - Grok 3 & 3 Mini No SDK needed. Works with curl, Python, TypeScript. Free: 100 evals/month, no card. Built by a solo dev from Tokyo.
Does the same job
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- GVGemini vs. ChatGPT vs. Claude AI2024 · geminivsgpt.com · ▲6
Hey HN, I just updated my project that compares some LLMs. It uses your prompt for all the models and runs at the same time. You can see the results being generated in real-time and decide what's the best for your use case. I'm open to any suggestions and feedback. Thanks!
- SWSimple wrapper for Chrome's built-in local LLM (Gemini Nano)2025 · github.com · ▲33
Chrome now includes a native on-device LLM (Gemini Nano) starting in version 138. I've been building with it since it was in origin trials, it's powerful but the official Prompt API is still a bit awkward: - Enforces sessions even for basic usage - Requires user-triggered downloads - Lacks type safety or structured error handling So I open-sourced a small TypeScript wrapper I originally built for other projects to smooth over the rough edges: github: https://github.com/kstonekuan/simple-chromium-ai npm: https://www.npmjs.com/package/simple-chromium-ai…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Launched alongside, March 2026
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
