kimi-k2.6

By price, kimi-k2.6 ranks 45th cheapest of 79 on input and 46th on output. Its quality score of 90 ranks 27th of 79, and on value (quality score ÷ output price) it ranks 46th of 79. Its blended rate sits 46% below the catalog average.

In real billing terms: a support chatbot handling 50,000 turns a month (2,000 input / 500 output tokens each) costs about $195 per month on kimi-k2.6, while a RAG workload of 20,000 queries (8,000 input / 800 output tokens each) runs about $216. Because output is priced 4.2× input here, long-generation tasks scale cost fastest — cap output length before you optimise anything else.

A 262K context window holds roughly 349 pages of A4 text (at ~750 tokens per page), ranking 43rd of 79 here. That covers typical RAG and multi-turn chat, though book-length inputs still need retrieval and chunking. Output runs at about 85 tok/s (53rd fastest of 79), acceptable interactively, though long responses will feel slow.

We track 2 Moonshot AI models, and kimi-k2.6's blended rate sits 56% below that provider's average. If your task tolerates a little less headroom, Hunyuan HY3 (Tencent) blends about 94% cheaper at a quality score of 87, and is worth benchmarking first. Going the other way, qwen3.5 scores 91 on quality for about 93% less.

The closest model in Moonshot AI's line-up is kimi-k3 ($3.00 input / $15.00 output, quality 95). Against it, kimi-k2.6 scores 5 points lower on quality, runs 25 tok/s faster (about 42%), has a 738K smaller context window, costs 250% less blended. When two models sit this close on price, the deciding factor is usually the quality ceiling — run both against your real prompts before picking.