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.
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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
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. @scaling01, @scaling01
The forecast-to-result reversal was notable. Before testing, @teortaxesTex predicted AA 52, Vals 65, CritPt V2 76, Terminal-Bench 4.0 49, WeirdML V3 approximately 20 officially/28 best, and ECI 164, while expecting practical performance below Sonnet 5.5 and GPT-6.1 Sol. After launch, the same commentator acknowledged results above the forecast, then accepted the same-cost-as-K3, roughly ten-points-over-DeepSeek-1017 AA outcome. Those initial numbers were explicitly predictions, not leaked measurements. @teortaxesTex, @teortaxesTex, @teortaxesTex, @teortaxesTex
Pricing: cheaper tokens do not guarantee cheaper completed work
Standard first-party pricing is $2/M input tokens, $10/M output tokens, and $0.20/M cached-input tokens, down one-third from K3’s $3/$15/$0.30. The official Chinese-account summary gives RMB 13 uncached input, RMB 68 output, and RMB 1.3 cached input per million tokens. Highspeed is described as twice standard pricing. @ArtificialAnlys, @ZhihuFrontier
Three different cost views are important:
Workload/source
Reported K3.1 Pro cost
Comparison
AA Intelligence Index
$1.98/task
Roughly unchanged from K3
Arena WebDev
$1.23 median/task
29% below K3
AA Cyber Index
$2.81/task
15% above K3; much more than DeepSeek 1017
On the Intelligence Index, K3.1 Pro generated 55K output tokens per task, versus K3’s 48K—approximately 14% more—offsetting some of the token-price reduction. On AA’s cyber suite, roughly twice the input-token consumption contributed to higher task cost despite lower rates. @ArtificialAnlys, @ArtificialAnlys
Arena’s more detailed comparison puts its $1.23 median task cost:
74% below Opus 5 Max, with one additional preference point.
24% below GPT-6 Sol Max, with 11 additional points.
47% below Qwen3.8-Max, with 20 additional points. @arena
Launch commentary emphasized standard rates 50% below Opus 5.5’s quoted $4/$20 and 80% below Astra’s $10/$50. Those comparisons concern per-token list prices, not measured cost per successful task. @kimmonismus
Terminal-Bench cost claims were inconsistent across reactions: one called it better than Opus 5.5 at about one-third the cost, while Cline described near-parity at half the cost. Different harnesses, settings, or cost accounting could explain this, but the supplied text does not reconcile them. Cline later announced availability through ClinePass, a $9.99/month plan advertising discounted open-weight access. @Yuchenj_UW, @cline, @cline
Cybersecurity: training-data removal, yet the strongest public API result
Moonshot’s distinctive safety intervention was to remove cyber-related data from mid-training and reinforcement learning, rather than relying solely on inference-time refusals. Posts quoting the report say an internal score fell from 90.5% to 81%, a 9.5-percentage-point reduction, while coding and other capabilities continued improving. Moonshot’s stated counterfactual was that downstream cyber capability would otherwise increase substantially. @scaling01, @maharshii, @cline
Early reactions called the model “nerfed” and compared its internal cyber score unfavorably with GPT-5.6 Sol. Other posts likened it to an earlier Mythos Preview, or speculated that an unrestricted internal version might match stronger proprietary systems. These cross-model equivalences and internal-version estimates were commentary, not independent findings. @teortaxesTex, @teortaxesTex, @scaling01, @scaling01, @scaling01
K3.1 Pro improved 23 index points over K3’s 41%, driven especially by CyberGym’s 58%→95% increase. AA observed no safety refusals on its tasks. @ArtificialAnlys, @ArtificialAnlys
This separates two mechanisms often conflated in the discussion:
Training-time capability reduction: Moonshot’s intervention may lower particular exploit capabilities relative to its own counterfactual model.
API refusal policy: AA’s public defensive tasks were still answered without refusals.
AA cautions that p
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