01 / FAMILIESIdentify families before individual models
Common international families include OpenAI GPT and Codex, Anthropic Claude, Google Gemini and Meta Llama; Chinese teams include DeepSeek, Alibaba Qwen and Moonshot Kimi. Each family typically has variants for speed, cost, reasoning depth and multimodality. A company, product and model name are not interchangeable: ChatGPT is a product while GPT is a model family, and a vendor can offer flagship, fast, low-cost and specialized models at once.
02 / SNAPSHOTTreat names as a snapshot, not a live menu
This page is a July 26, 2026 snapshot, not a continually current ranking. At that check date, official pages featured the GPT-5 family and code, audio/video and open-weight variants; Gemini distinguished stable and preview lines; DeepSeek listed V4-Pro and V4-Flash; Qwen and Kimi published open weights through official channels. Names will change. Before real use, open the vendor’s official model page to verify availability, preview status, context and output limits, prices, input types and tool capability. An article teaches how to read the menu; the official page tells you what is served today.
03 / CHOICEChoose with five questions
There is no eternally strongest model, only a fit for present task, cost, region, privacy and tools. For daily rewriting and summary, do not default to the most expensive flagship. Complex research, repositories, multimodal files or long tasks may need a higher tier. Ask: is this writing, retrieval, code, image, audio or real operation; is a web product enough or do you need desktop, CLI, API or local deployment; are region, payment, account and enterprise policy officially supported; can material go to cloud or must it remain local; and is cost a subscription, tokens, or local hardware?
04 / COMPARECompare with fixed tasks and acceptance criteria
Do not compare models through impressions from different questions. Prepare three to five fixed tasks from your work, give every model the same material and requirements, then record correctness, sources, duration, revision count and whether the result is usable. Repeating one small test suite is closer to your real choice than a composite leaderboard.
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