
What happened
Kimi launched K3, a 2.8 trillion parameter open model with a one million token context window. In Kimi's benchmarks, K3 trails only Claude Fable 5 and GPT-5.6 Sol but beats all other tested systems including Claude Opus and Chinese rival GLM-5.2. Independent testing by Artificial Analysis scores K3 at 57 on the Artificial Analysis Intelligence Index, placing it fourth behind Fable 5 (60), GPT-5.6 Sol (59), and Opus 4.8 (56). Full model weights are scheduled for release by July 27.
Why it matters
K3 signals the end of ultra-cheap Chinese AI. Kimi's pricing—$0.30 per million input tokens (with cache hit) and $15.00 for output—is nearly 19 times higher than its predecessor K2.6 ($0.16 input, $4.00 output). Chinese providers overall are raising prices for frontier models. At $0.94 per task, K3 lands in the same range as GPT-5.6 Sol ($1.04) but costs roughly half of Claude Opus 4.8 ($1.80), positioning it as a competitive midrange option rather than a price-leader.
What to watch
K3 shows higher hallucination (51 percent) than its accuracy improvement (46 percent, up from 33 percent on the AA-Omniscience Index), and it uses a mixture-of-experts architecture activating only 16 of 896 experts. The model is available now via Kimi.com, mobile apps, and Kimi Code; a planned Kimi Hosted Agent platform with isolated environments for long-running tasks is in waitlist signup.
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Kimi K3 represents a significant shift in Chinese AI economics. While the model delivers performance competitive with top-tier Western proprietary systems, its pricing strategy breaks sharply with the race-to-the-bottom dynamic that characterized Chinese AI for the past year. At $0.30–$3.00 per million input tokens and $15.00 per output, K3 is priced to compete with Claude Sonnet 5 (identical pricing) and sit between GPT-5.6 Sol ($0.50–$5.00) and open-weight competitors like DeepSeek V4 Pro ($0.04). This pricing reflects both the computational cost of training and serving a 2.8 trillion parameter model and a broader market signal: frontier AI models—whether proprietary or open-weight—are no longer treated as loss-leader commodities.
The performance data underscore K3's position in the upper midrange. Artificial Analysis's independent testing places K3 at 57 on the Intelligence Index, a clear fourth place, but the margin matters: it trails Opus 4.8 by only one point, and on agentic tasks (Elo 1,668), it substantially beats both Opus 4.8 and GPT-5.5. Kimi's own benchmarks show K3 winning roughly one-sixth of tests across a 35-test suite and placing second or third in most others. However, Artificial Analysis flags a troubling trade-off: K3's hallucination rate climbed to 51 percent even as accuracy improved to 46 percent, a 12-point jump from K2.6, suggesting the model generates more plausible-sounding but false outputs.
The open-weights release by July 27 will test whether market demand supports non-cheap open models. K3's efficiency gains—a mixture-of-experts design activating only 16 of 896 experts, and a new Kimi Delta Attention architecture enabling 6.3× faster decoding on million-token contexts—suggest the model was engineered for cost-effective inference at scale, not merely raw capability. For businesses running agentic workloads, the positioning as a platform for "Vision in the Loop" code generation and long-horizon task completion signals Kimi's intent to compete on developer utility rather than price, a marked departure from the commodity positioning of earlier Chinese models.
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