Kimi K3: What Moonshot’s 2.8T Flagship Means for Builders

Moonshot AI’s Kimi K3 is the lab’s most capable flagship to date: a 2.8-trillion-parameter open 3T-class model with a 1-million-token context window and native vision. If you build agents, ship frontend/product code, or do long research sessions, here is what matters in practice — what it is good at, what it costs, and how to adopt it without getting surprised by the launch constraints.

Need lower latency on the same intelligence? See the companion piece on Kimi K3 Fast (moonshotai/kimi-k3-fast) — same weights, higher-throughput serving, higher per-token price.

Sources for the facts below: Moonshot’s Kimi K3 tech blog and the official Kimi K3 quickstart.

What Kimi K3 is

Kimi K3 (moonshotai/kimi-k3 on OpenAI-compatible gateways; kimi-k3 on Moonshot’s own API) is a sparse Mixture-of-Experts model that Moonshot describes as:

• 2.8T total parameters, effectively activating 16 of 896 experts
• Built on Kimi Delta Attention (KDA) and Attention Residuals
• Native visual understanding (text + image input; text output)
• 1,048,576-token context with flat, non-tiered context pricing
• Aimed at long-horizon coding, knowledge work, and deep reasoning

Moonshot is explicit that overall performance still trails the strongest proprietary systems it compares against (Claude Fable 5 and GPT-5.6 Sol), while claiming frontier-level results across its evaluation suite and strong gains versus earlier open models. Treat vendor benchmarks as directional; the useful takeaway is the job family K3 is optimized for: multi-hour engineering sessions, large repos, tool use, and research workflows that mix text with screenshots.

Full open weights are scheduled by July 27, 2026, with a fuller technical report promised alongside that release. Until then, API and product surfaces are the practical path.

Specs that affect real work

Property	Kimi K3
API model id (gateway)	moonshotai/kimi-k3
Fast / high-throughput id	moonshotai/kimi-k3-fast — K3 Fast guide
Official Moonshot id	kimi-k3
Total / active experts	2.8T MoE; 16 of 896 active
Context	1,048,576 tokens
Modalities	Text + image in → text out (official API also documents video via file upload)
Thinking	Always on; reasoning_effort: low / high / max (default max at launch)
Max completion tokens	Default 131,072; up to 1,048,576
Sampling	Fixed at launch (temperature=1.0, top_p=0.95, etc. — omit overrides)

Two integration details from the official docs are easy to miss:

1. Always return the full assistant message in multi-turn and tool loops — including thinking history. K3 was trained with preserved thinking history; dropping it (or mid-session switching from another model) can make quality unstable.
2. Vision on the official API does not accept arbitrary public image URLs; use base64 or Moonshot’s ms:// file references, and send content as an array of parts.

Pricing: where the bill actually lands

Official Kimi API list pricing for K3 (flat across the 1M window):

Token type	Price
Cache-hit input	$0.30 / MTok
Cache-miss input	$3.00 / MTok
Output	$15.00 / MTok

Widely published K3 Fast serverless list rows are about 1.5x standard ($4.50 / $0.45 / $22.50). Fast buys throughput, not a smarter model — see the K3 Fast article.

Moonshot reports >90% cache hit rates on coding workloads on its infrastructure when long prefixes stay stable. That is the main cost lever: keep system prompts, repo digests, and tool schemas unchanged across turns so later requests can hit cache. Output is the expensive side — long agent traces with verbose reasoning at max effort add up quickly.

On this platform, moonshotai/kimi-k3 is already listed; check the live retail quote on Pricing before you budget, since gateway retail can differ from list.

When K3 is the right call

Prefer Fast (moonshotai/kimi-k3-fast) when wall-clock time dominates — interactive agent loops where standard K3 quality is already good enough. See when to pay for K3 Fast.

Reach for K3 when you need:

• Long-running coding agents that navigate large repositories and terminal tools
• Frontend / product work that benefits from “vision in the loop” (screenshots → revise UI)
• Research and knowledge-work agents that produce structured reports, charts, or long multi-step analyses
• A frontier-capable open-weight path once the July 27 weights land (self-host or partner inference)

Prefer a cheaper Kimi or another model when you need:

• Short chat, high QPS, or casual autocomplete — K2.x-class models are usually enough
• Strict “do nothing without permission” agents — Moonshot notes K3 can be over-proactive on ambiguous tasks; constrain it in the system prompt / AGENTS.md
• Zero-surprise sampling controls — K3’s fixed sampling at launch may not fit every eval harness

Practical adoption checklist

• Start with reasoning_effort="high" (or low) for cost-sensitive paths; reserve max for hard jobs after you measure quality vs. spend.
• Design for prompt caching: one stable long prefix, many short questions.
• Use an OpenAI-compatible client and set model to moonshotai/kimi-k3 if you already route through a unified gateway:

curl https://openfluxhub.com/v1/chat/completions \
  -H "Authorization: Bearer $YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/kimi-k3",
    "messages": [{"role": "user", "content": "Summarize the risks in this migration plan."}]
  }'

• Keep tool-call loops faithful: append the complete assistant message, then one tool result per tool_call_id.
• If you self-host later, Moonshot recommends supernode-style deployments with 64+ accelerators — plan infra separately from API trials.

Bottom line

Kimi K3 is Moonshot’s bid for open frontier intelligence: 2.8T-scale MoE, 1M context, native vision, and API access now, with weights promised by July 27, 2026. It is not positioned as the single best closed model in the world — Moonshot says as much — but it is a serious option for builders who need long-horizon coding and knowledge-work agents at a transparent $3 / $15 token price with aggressive cache discounts.

For latency-bound loops on the same intelligence, escalate to Kimi K3 Fast.

moonshotai/kimi-k3 is live on this platform. Open the model page, create a key, and run it against one real repo or research brief before you change production defaults.