Shannon 2 Pro

When a task's difficulty is the constraint — not its volume — you want every bit of the foundation's capability and a window into how it reasons. Shannon 2 Pro runs K2.7 at full precision and surfaces its native thinking traces, so you can follow, verify, and steer multi-step reasoning instead of trusting a black box. It is the build we reach for on the problems that actually matter.

01The foundation: Kimi K2.7

Shannon 2 Pro is based on Kimi K2.7, Moonshot AI's open-weights flagship (released June 12, 2026): a sparse Mixture-of-Experts model where only a small fraction of a trillion parameters activate per token — frontier-class quality at a tractable serving cost.

02Native chain-of-thought — the heart of Pro

K2.7 reasons natively before it answers. Pro exposes those reasoning traces instead of hiding them, which changes how you work on hard problems:

• Visible thinking — follow the model's multi-step reasoning and catch a wrong turn early.
• Steerable — intervene mid-reasoning on long, branching tasks.
• Full precision — no quantization loss on the calls where the ceiling matters.
• Native Skills — compose reusable capabilities into complex workflows.

The foundation also trims reasoning-token usage roughly 30% versus the previous generation, so transparent reasoning doesn't have to mean runaway cost on long agent runs.

03Frontier distillation

Pro and Lite share one post-training pass: 30,000 curated, frontier-grade reasoning and instruction examples. The goal is to sharpen how the model answers — cleaner instruction-following, more consistent formatting, better tool-call discipline, and fewer needless refusals on legitimate professional work — not to change what it knows.

04How Pro stacks up

Pro's story is capability. On MCPMark Verified — real-world agentic software tasks, and the only public benchmark where the K2.7 foundation, Claude Opus 4.8, and GPT-5.5 all report numbers on the same test — the foundation lands between the two closed leaders:

Pro beats Claude Opus 4.8 on real-world agentic tasks and trails GPT-5.5 — while costing a fraction of either. We show the loss as readily as the win, because that is what makes the numbers worth trusting.

Metric	Shannon 2 Pro	Claude Opus 4.8	GPT-5.5
Agentic (MCPMark)	81.1	76.4	92.9
Open weights	Yes	No	No
Output / 1M tokens	$4.00	$25.00	$30.00
Context window	256K	1M	~1M

MCPMark Verified & list API prices, June 2026. K2.7 figures are Moonshot-reported; independent third-party benchmarks are pending. GPT-5.5 and Claude Opus 4.8 are shown for reference.

05Minimal censorship, maximum responsibility

Shannon 2 Pro is tuned for minimal censorship: on legitimate security, red-team, and research tasks it stays direct instead of refusing by reflex. It is a professional tool — access is gated to verified professionals, usage is continuously audited, and the model is operated under our Responsible Use Policy.

06Where Pro shines

• Deep exploit analysis — multi-step vulnerability research with visible reasoning.
• Long multi-file refactors — agentic coding across large codebases within 256K context.
• Dozens-of-turns agents — the highest ceiling for long-horizon autonomous work.
• Skills-driven workflows — compose reusable capabilities for complex tasks.

07Frequently asked questions

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Sources: Moonshot AI (Kimi K2.7) · K2.7 vs GPT-5.5 vs Claude Opus 4.8 comparison · Independent K2.7 pricing analysis. K2.7 benchmarks are Moonshot-reported and provisional pending independent verification.