Benchmark AI on your actual code (GPT-5, Claude, Grok, Gemini, o3)
I built CodeLens.AI - a tool that compares how 6 top LLMs (GPT-5, Claude Opus 4.1, Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, o3) handle your actual code tasks. How it works: - Upload code + describe task (refactoring, security review, architecture, etc.) - All 6 models run in parallel (~2-5 min) - See side-by-side comparison with AI judge scores - Community votes on winners (blind voting) - Each evaluation gets reflected in the overall AI model leaderboard, showing us best ones Why I built this: Existing benchmarks (HumanEval, SWE-Bench) don't reflect real-world developer tasks. I wanted to…
Benchmark AI on your actual code (GPT-5, Claude, Grok, Gemini, o3) launched on October 11, 2025 with 7 votes, #2,779 of 8,178 launches that month and more than 75% of that year's launches. The site was not reachable when checked in September 2026.
What it does
In the maker’s words, at launch
I built CodeLens.AI - a tool that compares how 6 top LLMs (GPT-5, Claude Opus 4.1, Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, o3) handle your actual code tasks. How it works: - Upload code + describe task (refactoring, security review, architecture, etc.) - All 6 models run in parallel (~2-5 min) - See side-by-side…
Does a similar job
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Claude Code and Claude 3.7 Sonnet 2025 · anthropic.com · ▲875Claude Opus 4.7 is Anthropic's most capable generally available model, released April 16, 2026. It outperforms Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on key benchmarks including agentic coding, multidisciplinary reasoning, scaled tool use, and agentic computer use. It is benchmarked below Claude Mythos Preview — which remains restricted to Project Glasswing partners. https://vucense.com/ai-intelligence/ai-tools/claude-opus-4-7...
Live AI Design BenchmarkFeb 2026 · shuffle.dev · ▲81Hey HN, We built a different kind of AI benchmark for UI generation. Instead of static leaderboards or curated screenshots, you can watch multiple models generate the same design live, side-by-side, and decide which output is actually better. Under the hood, we call AI models from Anthropic (Opus), OpenAI (GPT), Google (Gemini), and Moonshot AI (Kimi). Each model generates a real, editable project using Tailwind CSS (not screenshots or canvas exports). You can export it for Next.js, Laravel (Blade), Symfony (Twig), WordPress, or plain HTML. What we noticed building this: * Popular benchmarks…
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Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
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Source: Show HN post ↗ · launch text quoted in part
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I went down the rabbit hole on a side project and ended up building this: Strange Attractors(https://blog.shashanktomar.com/posts/strange-attractors). It’s built with three.js. Working on it reminded me of the little "maths for fun" exercises I used to do while learning programming in early days. Just trying things out, getting fascinated and geeky, and being surprised by the results. I spent way too much time on this, but it was extreme fun. My favorite part: someone pointed me to the Simone Attractor on Threads. It is a 2D attractor and I asked GPT to extrapolate it to…
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