Moonshot发布Kimi K3.1 Pro，承诺11月4日开源

2 months ago we covered Z.ai CEO Prof Jie Tang’s prediction that we’d get a Fable-class open weights model by year end. 2 weeks ago Meta achieved a Fable-class model in closed source, but, with 2.5 months left to go, we finally got it - a 2.8T open weights model beating Fable 5.1, not just Fable 5, at that:

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with even better efficiency:

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You can tell Kimi is proud of their latest model by how fancy their landing page is:

As usual we don’t get much research detail - the update is somewhat similar to Z.ai’s 5.2-5.3 bump - a minor version bump for a lot more post-training:

Kimi K3.1 Pro is a post-trained version of Kimi K3, with substantially larger post-training compute. To enable large-scale joint RL across hundreds of downstream domains, we optimized every layer of the stack — the RL framework, long-context inference, training efficiency, sandbox infrastructure, rollout scheduling, and a shared platform for environment quality checks — so that each increment of RL compute delivers stable, predictable returns. As a result, K3.1 Pro achieves consistent gains in every domain.

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Top Story: Kimi K3.1 Pro launch and reactions

What happened

Moonshot launched Kimi K3.1 Pro with near-frontier independent benchmark results, a substantial increase in model size, and an open-weight release promised for November 4, 2026.

• The model: Moonshot describes K3.1 Pro as a multimodal, “3T-class” mixture-of-experts model with 2.8 trillion total parameters, 104 billion active parameters, and a 1-million-token context window. Its advertised strengths include full-project coding, Blender/CAD work, financial modeling, and long-running agents. @Kimi_Moonshot
• Availability: Standard and Highspeed versions launched across Kimi’s website/app, Kimi Work, Kimi Code, and API. Moonshot claims Highspeed preserves intelligence while generating output 2–3× faster; the official-account summary says it costs twice as much. The launch also includes Kimi Hosted Agents, a managed harness with a 24/7 execution environment for long-running work and multi-agent collaboration. @ZhihuFrontier, @ZhihuFrontier
• Independent general-capability result: Artificial Analysis placed K3.1 Pro fourth on its Intelligence Index, scoring 55, behind Opus 5.5 at 58 and Sonnet 5.5 and Muse Wonder Full at 56. Its strongest improvements were agentic rather than uniformly distributed across knowledge and science tests. @ArtificialAnlys
• Independent coding result: Arena placed it #8 in Code Arena: WebDev, at 1,692 points, up nine places and 37 points over Kimi K3. Moonshot returned to Arena’s top four labs. @arena
• Independent cyber result: Despite deliberately reducing cyber-related training, it subsequently scored 64% on Artificial Analysis’ Cyber Index—the highest publicly accessible result and second overall, behind trusted-access GPT-6 Sol Daybreak Blue at 69%. @ArtificialAnlys
• The reaction split: Enthusiasm about a prospective open-weight frontier contender was followed by concerns about inference economics, weaker results on some interactive tasks, benchmark presentation, and allegations of undisclosed paid promotion. These are distinct issues: benchmark quality does not establish deployment economics, and promotional misconduct allegations do not establish model capability. @simonw, @maxbittker, @kimmonismus

Release status, architecture, and what “open” means here

At launch, the API was available; the weights were not. Moonshot’s announcement promises weights by November 4, 2026. The supplied tweets do not establish the eventual license, redistribution conditions, commercial-use restrictions, checkpoint precision, or complete inference requirements. Consequently, “soon-to-be open-weight” is more precise than the “OSS” or “open source” language used in several reactions. Artificial Analysis explicitly recorded the weights as unpublished. @Kimi_Moonshot, @ArtificialAnlys

The release sequence generated confusion:

• Early reports predicted a release “today” and potentially the strongest open-weight model, explicitly on rumors/leaks; a follow-up reiterated positive hearsay. Neither was an evaluation. @kimmonismus, @kimmonismus
• Teasers triggered speculation about November 5, including whether Moonshot would monetize website access before publishing weights. The official announcement subsequently specified November 4. @scaling01, @scaling01, @bigeagle_xd
• Initial users reported availability on kimi.com before kimi.ai; another later reported obtaining access. These are rollout observations, not evidence of a lasting regional restriction. @teortaxesTex, @teortaxesTex
• Repeated complaints about suspense and guesses about a symbolic launch time reflected the launch atmosphere, not confirmed scheduling information. @teortaxesTex, @teortaxesTex, @teortaxesTex

Architecturally, 104B active parameters do not make this equivalent to storing a 104B dense model: deployment must accommodate the much larger expert pool, plus runtime state. As an illustrative calculation—not a measured configuration—2.8T parameters alone imply approximately 5.6 TB at 16-bit or 1.4 TB at 4-bit, before quantization metadata and serving overhead. The supplied posts disclose no layer layout, expert routing configuration, attention design, training-compute budget, or measured full-context serving footprint.

That matters for the “local model” narrative. One enthusiastic post called it Fable 5.1 intelligence available locally, then corrected that weights would arrive November 4. Separately, @giffmana warned against calling trillion-parameter models “local” when deployment requires substantial accelerator infrastructure. Self-hostable and consumer-device-local are different properties. @kimmonismus, @kimmonismus, @giffmana

No Flash model was announced in the supplied official launch. Reactions ranged from disappointment to speculation that the “Pro” designation implies a smaller version later. An earlier theory suggested the naming reflected serving constraints; none of these posts establishes a Flash release or its specifications. @TheZachMueller, @teortaxesTex, @eliebakouch

Independent evaluations: substantial agentic gains, smaller knowledge gains

Artificial Analysis supplies the strongest broad independent evidence in this collection. Its reported comparison with Kimi K3 is:

AA additionally reports accuracy rising from 48% to 51% and hallucination rate falling from 53% to 44% on AA-Omniscience. This is a nine-percentage-point hallucination reduction on that evaluation—not the same measurement as Moonshot’s advertised 65% reduction in finance work. @ArtificialAnlys

Once weights are released, AA says its leading-open-versus-proprietary Intelligence Index gap would narrow from 12 points to three. Nathan Lambert highlighted the higher score than Fable 5.1 and Astra 6; Simon Willison described it as the first prospective open-weight release to compete this credibly with the current frontier. @natolambert, @simonw

Arena’s independent preference evaluation offers a complementary, narrower picture:

• WebDev: #8, 1,692 points, versus K3’s #17 and 1,655.
• Brand & Marketing: #7, up from #9.
• Simulations: #9, up from #20.
• Arena says it improved across all categories and would become its highest-ranked open-weight model after publication, overtaking DeepSeek V4.1 Flash.
• At these settings it roughly matches Opus 5 Max, not necessarily the newer Opus 5.5. @arena