Kimi K3: Moonshot AI's 2.8T Open Model for Developers

Moonshot AI shipped Kimi K3 on July 16, 2026 — a 2.8-trillion-parameter Mixture-of-Experts (MoE) model with a 1-million-token context window, priced at $3.00 per million input tokens and $15.00 per million output tokens through Moonshot's API. For developers, the headline is less about the parameter count and more about access: a near-frontier coding and agent model is available today at roughly a fifth of Claude Fable 5's output price, with weights following on July 27, 2026 under a permissive Modified MIT license.

What Moonshot actually shipped

According to Moonshot AI's own announcement, Kimi K3 is built on two new architectural pieces designed to scale attention more efficiently than prior Kimi models:

• Kimi Delta Attention (KDA) — a hybrid linear-attention mechanism the company describes as an efficient foundation for scaling attention across long sequences.
• Attention Residuals (AttnRes) — a replacement for standard residual connections that selectively retrieves representations across model depth instead of accumulating them uniformly.

Routing uses what Moonshot calls a Stable LatentMoE framework, activating 16 of 896 experts per token — an MoE design that keeps per-token inference compute far below what a 2.8-trillion-parameter dense model would require, even though the full parameter count still needs a multi-GPU serving setup to run. The model also ships with native vision support, Gated MLA, a Sigmoid Tanh Unit (SiTU) activation, and quantization-aware training using MXFP4 weights with MXFP8 activations, per Moonshot's technical write-up.

Pricing: how it compares

Moonshot's rate card, confirmed on its own blog, is flat regardless of context length used:

That output price undercuts Claude Fable 5's $50 per million output tokens by more than 3x, and sits below GPT-5.6 Sol's pricing too — see our GPT-5.6 developer guide for the OpenAI side of that comparison. Moonshot itself is candid that Kimi K3 does not lead on every benchmark: the company's own reported numbers put Kimi K3 at 67.5 on DeepSWE versus Claude Fable 5's 70.0, while Kimi K3 scores 88.3 on Terminal-Bench 2.1, 81.6 on MMMU-Pro, and 93.5 on GPQA-Diamond. Those figures are vendor-reported by Moonshot, not independently verified, and should be treated as a starting point for your own evaluation rather than a final ranking.

Calling the API

Moonshot's API follows the now-common pattern of an OpenAI-compatible interface, so switching an existing integration mostly means changing the base URL and model name:

from openai import OpenAI
 
client = OpenAI(
    base_url="https://api.moonshot.ai/v1",
    api_key=os.environ["MOONSHOT_API_KEY"],
)
 
response = client.chat.completions.create(
    model="kimi-k3",
    messages=[
        {"role": "user", "content": "Summarize the open pull requests in this repo."}
    ],
)

Because the request shape matches the OpenAI SDK, agent frameworks that already target OpenAI- or Anthropic-compatible endpoints — similar to the coding agents covered in our AI coding agents practical guide — should work by swapping credentials and the model identifier, without a rewrite of integration code.

What changes for developers

• A cheap, near-frontier coding model becomes available without self-hosting. At $15/M output, the same token volume that costs $50 against Claude Fable 5 costs roughly $15 against Kimi K3 — before accounting for any difference in output quality or token efficiency per task.
• Self-hosting is a July 27 question, not a today question. The API is live now; the Modified MIT-licensed weights aren't released until July 27, 2026, and even then a 2.8T-parameter MoE model needs serious multi-GPU infrastructure to serve, so most teams will keep using the hosted API rather than running it themselves.
• Benchmark gaps are real but narrowing. Kimi K3 trails Claude Fable 5 on Moonshot's own DeepSWE numbers — plan to benchmark your specific workload rather than assume parity, the same caution that applies when evaluating any new entrant like the one in our Grok 4.5 developer guide.
• Pricing pressure keeps compounding. Kimi K3 is the latest in a string of 2026 releases undercutting incumbent flagship pricing — worth factoring into any multi-model routing strategy for cost-sensitive workloads.

A pragmatic first step

Route one narrow, high-volume task — bulk summarization, a single coding-agent step, or classification — through the OpenAI-compatible endpoint at model="kimi-k3", and compare real cost and output quality against whatever model currently handles that step before committing anything mission-critical to it.

Further reading

• GPT-5.6 Sol, Terra and Luna: A Developer's Guide
• Grok 4.5: Cursor's Co-Trained Coding Model, Explained
• AI Coding Agents: A Practical Guide to Working With Them