[AINews] Kimi 3.1 Pro 104BA-2.8T: Fable-5.1-Class Open Weights narrows Closed-AI lead to 1 month... and Pro Highspeed API with 2-3× speed

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: View post on X ↗ with even better efficiency: View post on X ↗ 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. AI News for 10/22/2026-10/23/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews' website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! AI Twitter Recap 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 A separate aggregation posted a preliminary ECI of 164.7 , with a wide 90% bootstrap interval of 152.5–169.0 , based on published benchmarks. The poster subsequently suggested 160–165 as a reasonable range. This is an uncertain aggregation, not an additional independently executed task suite.