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Kimi K3 (Moonshot AI) is the largest open-weight frontier model in Tess: 2.8T total parameters (104B active per token), native vision, and 1M context. Use it for long-horizon coding, agents, and multimodal knowledge work.

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What changed vs Kimi K2.6

MoE architecture (896 experts, 16 active per token). Tess exposes three reasoning levels; Max is the deepest (Moonshot’s default at launch).

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Pricing (Tess credits)

Values follow Models and Costs (credits per 100 tokens). Same rate across reasoning levels: Cached context reads are billed at a lower rate than a full input pass.
Screenshot placeholder — model picker: Capture the chat model selector with Kimi K3 (Low / High / Max) visible.

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Ideal use cases in Tess

  1. Long-horizon coding — large repos, multi-file refactors, code agents
  2. Persistent agent workflows with tool loops
  3. Multimodal knowledge work (documents, images, and video in one context)
  4. Deep reasoning with Kimi K3 Max
  5. Open-weight frontier workloads that still need frontier-class capacity
  6. Long sessions (1M context) without switching models
Best practices
  • Use Low for drafting and high-volume passes; High as the default; reserve Max for planning-heavy tasks.
  • K3 costs more than K2.6 — don’t swap it in as a blanket default on cheap extraction jobs.
  • Lean on cached context in long threads to keep input cost down.
See also: Models and Costs · Kimi K3 (Moonshot).