K3 slots in as one of the best open-weight agent, simply behind Claude Fable 5 on khelaghor প্রোমো reasoning depth and roughly even with GPT-5.6 Sol on terminal work, at a price between them. It is behind Gemini three.1 Pro’s video stack, however no different open-weight mannequin even performs this game but, and that alone adjustments what open fashions can be utilized for. The Opus 4.7-plus community chatter feels proper for difficulty ceiling, though Claude Fable 5 still debugged one edge case K3 patched around. In Opposition To the identical task, GPT-5.6 Sol was sooner but shallower, checking fewer sources. I ran K3 via the Kimi app, Playground, and API during launch week, utilizing the same four workloads from our other reviews so the comparisons keep honest.
Customers observed that K3 sometimes references Anthropic’s specific security and content material policies in its inner “thinking” traces, suggesting Claude outputs have been utilized in K3’s reinforcement studying section. Kimi K3 is priced at $0.30/MTok for cache-hit inputs, $3.00/MTok for cache-miss inputs, and $15.00/MTok for outputs. Whether Or Not or not the distillation questions ever get resolved, that pricing and efficiency combination goes to strain every lab building on the belief that frontier intelligence stays expensive.
- K2.7’s coding worth is exactly why it featured in our GLM-5.2 vs Claude vs GPT-5.6 vs Kimi coding comparability, and that evaluation nonetheless holds for groups optimizing price per pull request.
- The full experience will depend on the files and instruments around it, but for everyday data work it is a model I would put in the first group to try.
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- A million uncached input tokens and 50,000 output tokens could be about $3.seventy five.
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On its reported agent evaluations, K3 scores 1,668 Elo on GDPval-AA v2, behind Fable at 1,760 and Sol at 1,748 however above Claude Opus 4.8 at 1,600. Moonshot’s broader table suggests K3 is strongest when coding, shopping, tools and visible reasoning meet. For deeper context on the 2 proprietary leaders, see Kingy AI’s analyses of GPT-5.6 Sol’s benchmarks and specs and Claude Fable 5’s benchmark and pricing image. The 1.78-point hole from Sol Max and 2.75-point gap from the Fable configuration are clearer, although nonetheless not a substitute for workload-specific testing. At listing worth, K3 input is 40% cheaper than Sol and its output is 50% cheaper. The model-specific K3 API quickstart separately confirms picture and video inputs, the one-million-token window and the launch-time request limits.
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As of launch day, there is not any K3 checkpoint in the company’s public model repository, no K3 license, no model card and no full technical report. For the 100K-input, 10K-output example, GPT-5.6 Sol’s listed charges produce an $0.eighty token bill and Fable 5’s produce $1.50, before software expenses or provider-specific caching. On its standardized 500-token performance workload, total response time is about 42.30 seconds. Synthetic Evaluation measures a 1.99-second time to the primary streamed chunk, adopted by about 32.24 seconds of reasoning before the first reply token. K3’s API begins streaming quickly, however the final reply doesn’t arrive in two seconds.
It is overkill for high-volume classifications, quick rewrites, or a task where a protracted answer is mostly waste. For research-oriented brokers, our deep analysis workflow information covers the habits value maintaining. Kimi Work and Kimi Code can add helpful tools across the model, while API users can build their very own route. The full expertise will depend on the recordsdata and instruments round it, however for everyday knowledge work it is a mannequin I would put in the first group to strive. The mannequin has the context headroom for the source material and the professional-work benchmarks to make the thought credible. Productiveness pickK3 has the context headroom for meeting notes, multi-document summaries, and analysis packs that might overwhelm a standard chat.