基于社区真实评测，对比GPT-4o、Claude、Qwen、Llama等主流模型的性能、成本和迁移难度。
The Hacker News thread “China’s open-weights AI strategy is winning” exploded with 723 comments in 48 hours. Developers aren’t debating geopolitics — they’re asking: “Should I switch from GPT-4o to Qwen 3.8? What about Kimi vs. Claude?” The signal is clear: the community is hungry for structured, actionable comparison data, but all they have is scattered anecdotes and subjective tweets.
Existing solutions like Chatbot Arena tell you which model wins in a blind test, but they don’t answer the real question: “What will it cost me to migrate? How hard is it to adapt my codebase? Which tasks will degrade?” Meanwhile, Artificial Analysis gives you raw pricing and latency numbers but zero guidance on the actual migration workflow. The gap is a decision tool — not another benchmark, but a migration advisor.
Now is the perfect moment. The open-weight ecosystem has reached critical mass: Qwen 3.8 matches GPT-4 on code generation, Kimi K3 rivals Claude 3.5 Sonnet on long-context reasoning, and Llama 4 is just around the corner. Every CTO and AI architect is facing a portfolio choice. The window to capture this “migration decision” market is open — and it won’t stay open long.
Choose from a curated list of 10+ popular models — including GPT-4o, Claude 3.5 Sonnet, Qwen 3.8, Kimi K3, Llama 4, and Mistral Large. Pick the task that matters most to you: code generation, text summarization, role-playing, or reasoning.
Our engine aggregates community consensus from HN, Reddit, and GitHub issues to produce a clear scoreboard: performance (task-specific), monthly cost (at 1M tokens), migration difficulty (1–5 stars), and API compatibility notes. All data is sourced from public discussions and official pricing pages.
Receive 3–5 concrete steps to execute the migration — from testing on HuggingFace to checking your codebase for `tool_use` API dependencies. No fluff, just a clear path from decision to deployment.
Not a single blogger’s opinion — we distill signals from hundreds of HN comments, Reddit threads, and GitHub issues to give you a balanced, data-driven view of how models really perform in the wild.
See exactly how much you’ll save (or spend) by switching. We calculate monthly costs at 1M tokens for each model pair, so you can justify the migration to your team with hard numbers.
We don’t just tell you which model is better — we give you a step-by-step checklist to actually make the switch, including code-level considerations and common pitfalls to avoid.