Kimi K3: Moonshot AI’s 2.8T Open-Weight Model — Release, Specs & Pricing (2026)

Moonshot AI shipped Kimi K3 on July 16, 2026, and it immediately reset expectations for what an open-weight model can do. At 2.8 trillion parameters it is the largest open-weight model released so far, and on independent testing it lands fourth among all frontier models — trailing only Claude Fable 5 and GPT-5.6 Sol, and edging past Claude Opus 4.8. For anyone building on open models, K3 is the first Chinese release that competes with the top U.S. systems on capability rather than just on price.

This guide covers what Kimi K3 actually is: the architecture, the specs, how to access it, what it costs, and where it fits. For the full head-to-head numbers against Fable 5, GPT-5.6 Sol, and Opus 4.8, see our companion Kimi K3 benchmarks comparison.

What is Kimi K3?

Kimi K3 is Moonshot AI's most capable model to date — a mixture-of-experts (MoE) large language model with 2.8 trillion total parameters, native multimodal (visual) understanding, and a one-million-token context window. Unlike the K2 series that preceded it, K3 ships with reasoning always on: Moonshot calls it "thinking mode," and the model reasons through problems by default rather than needing a separate reasoning variant.

It is positioned as an open-weight release. The model went live via Kimi's apps and API on July 16, 2026, with the full open weights scheduled to drop by July 27, 2026 — which is what makes the 2.8T parameter count so notable: nothing this large has been released with open weights before.

Kimi K3 key specifications

How does the Kimi K3 architecture work?

K3's headline efficiency trick is that it is enormous on paper but sparse in practice. Of its 896 experts, only 16 are activated for any given token — roughly 1.8% of the pool — so the compute cost of a forward pass is far lower than the 2.8T parameter count suggests. That is what lets a model this size run at a price comparable to mid-range Western models.

Two architectural innovations developed in-house at Moonshot do the heavy lifting:

• Kimi Delta Attention — a hybrid linear attention mechanism that Moonshot reports delivers up to 6.3x faster decoding, which is a large part of how K3 stays usable across a full 1M-token context.
• Attention Residuals — described as a drop-in replacement for standard residual connections. Moonshot reports a roughly 25% training-efficiency gain for under 2% additional compute overhead, which helped make training a 2.8T model economically feasible under China's compute constraints.

How much does Kimi K3 cost?

This is the biggest shift from the K2 line. Kimi K3 is priced at $3 per million input tokens and $15 per million output tokens (with a $0.30 cache-hit input rate) — the same tier as Anthropic's Claude Sonnet series. Its predecessor, Kimi K2.6, ran roughly $0.95 in / $4 out, so K3 is a substantial step up.

Commentators read this as the end of "super-cheap Chinese AI": Moonshot is now pricing on capability rather than undercutting. That said, on a per-task basis K3 can still come out cheaper than Western frontier models — Artificial Analysis measured about $0.94 per task for K3 versus $1.04 for GPT-5.6 Sol and $1.80 for Claude Opus 4.8 — because its answers tend to be more token-efficient.

How to access Kimi K3

1. Kimi apps: K3 is live on Kimi.com, the Kimi mobile apps, the Kimi Work desktop client, and Kimi Code.
2. API: Available directly from Moonshot and through aggregators such as OpenRouter. Note that at launch the API's reasoning effort supports only the max level, with additional levels coming.
3. Open weights: Once the full weights land (by July 27, 2026), you'll be able to self-host or run K3 through open-model providers — subject to the hardware needed for a 2.8T MoE.

If you're comparing open options more broadly, our Kimi K2.6 complete guide covers the prior generation, and the benchmarks piece below shows exactly where K3 sits against the closed frontier.

Who should use Kimi K3?

• Engineering teams building on open weights — K3 is the first open-weight model that credibly competes with GPT-5.6 Sol and Claude on frontier coding tasks (it took #1 on the Frontend Code Arena).
• Long-context workloads — repository-scale code navigation, large-document analysis, and log-heavy debugging benefit from the 1M-token window and strong long-context retention.
• Cost-sensitive agentic pipelines — the token-efficiency means real per-task costs can undercut Western frontier models even at a similar per-token price.

One caveat worth knowing: independent testing found K3's raw accuracy improved to about 46% (up from K2.6's 33%), but its measured hallucination rate also rose to around 51%. As with any frontier model, keep verification in the loop for factual work.

Frequently asked questions

When was Kimi K3 released?

Moonshot AI released Kimi K3 on July 16, 2026, via its apps and API. The full open weights are scheduled to be published by July 27, 2026.

How many parameters does Kimi K3 have?

Kimi K3 has 2.8 trillion total parameters, making it the largest open-weight model released to date. It uses a mixture-of-experts design with 896 experts, of which 16 are active per token.

Is Kimi K3 open source?

Kimi K3 is open-weight. Moonshot has committed to releasing the full model weights by July 27, 2026, so you'll be able to self-host it, subject to the substantial hardware a 2.8T MoE requires.

How much does Kimi K3 cost?

Kimi K3 is priced at $3 per million input tokens and $15 per million output tokens, with a $0.30 cache-hit input rate — roughly the Claude Sonnet tier and a notable increase over Kimi K2.6.

What is Kimi K3's context window?

Kimi K3 supports a 1-million-token context window, and it retains long-context performance well: on a 1M-token evaluation with no context management it scored 90.4.

Is Kimi K3 better than GPT-5.6 or Claude?

On the Artificial Analysis Intelligence Index, K3 scores about 57 — fourth overall, behind Claude Fable 5 (~60) and GPT-5.6 Sol (~59) but ahead of Claude Opus 4.8 (~56). It leads all of them on the Frontend Code Arena. See our full benchmark comparison for the details.

The bottom line

Kimi K3 is the moment open weights caught up to the closed frontier. It won't top every leaderboard — Fable 5 and GPT-5.6 Sol still lead the general-intelligence indexes — but a 2.8T open-weight model that ranks fourth overall, wins the frontend coding arena, and holds a full 1M-token context is a genuine milestone. If your stack is built on open models, K3 is the first one you can reach for without a capability compromise.

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