Chinese LLM Comparison

One table for a fast-moving, fragmented market

China's large-language-model market moves fast and is unusually fragmented: DeepSeek, Qwen (通义千问) from Alibaba, Doubao from ByteDance, Kimi from Moonshot, ERNIE (文心) from Baidu, Zhipu's GLM line and MiniMax each ship frequent updates at different price points. Comparing them one provider page at a time is slow and error-prone, because every vendor formats its pricing differently and quotes tokens in its own way. This tool collapses all of that into a single, sortable table: each row shows the model, its provider, the input price, the output price, the context window, and whether it is a reasoning or a general model. The numbers come from a shared data module that the whole RECATOOLS AI-tools family reads, loaded same-origin into your browser, so the comparison renders instantly and stays consistent with our other cost tools.

The most useful first move is to sort. Click the input-price header to rank models cheapest-first and immediately see where DeepSeek and the lighter Qwen tiers sit against the rest; click again to reverse it. Sort by context window instead when your real constraint is fitting a long document or a long conversation into a single request. Models whose price we have not yet confirmed against the provider's own page show "— / 待核实" and always sort to the bottom, so a pending figure never distorts the ranking. Because the table is just a view over structured data, it is honest about what it does and does not know — and it never pretends a missing price is zero.

Input price, output price and context — read all three

Reading the price columns correctly matters. Input price applies to the prompt tokens you send; output price applies to the answer tokens the model writes back. For almost every Chinese model the output price is the larger of the two — often by several times — so a long, generated answer costs far more than a long prompt. Reasoning models such as DeepSeek R1 amplify this: they emit a hidden "thinking" trace before the final answer, which means more output tokens and a higher effective bill, in exchange for stronger performance on hard reasoning, maths and code. For routine chat, summarisation and drafting, a cheaper general model is usually the better value. The context-window column is the third number to weigh: it sets how much text — input and output together — a single call can hold, and these models span roughly 130K to 260K tokens.

Treat every figure here as a provisional estimate, not a quote. The prices and context windows are gathered from public aggregators and are intended for fast side-by-side comparison; they can lag a provider's latest pricing, regional list price, or promotional tier. The right workflow is to use this table to shortlist two or three candidates on price, context and type, and then open each provider's official pricing page to confirm the current number before you build against it. The tool deliberately does nothing else: it reads our own same-origin data, renders the table locally, and never calls a model or uploads anything you do. That keeps it fast, private and dependable — a starting point for a decision, with the official page always the final word.