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On October 23, 2026, the Kimi official account released a 24-second Kimi K3.1 teaser video. In the footage, the model field in Kimi Code displayed "Model: Kimi K3.1," but the official release date, parameter scale, and pricing were not announced. The next day, Moonshot launched Kimi K3.1 Pro on the Kimi API. The official blog called it the first model in the K3.1 series and the strongest 3T-class open-weight model to date, with a context window of 1 million tokens. It also introduced Kimi K3.1 Pro Highspeed, which is 2 to 3 times faster, and the model has already launched on Kimi.com, Kimi Work, Kimi Code, and the Kimi API, with the full weights scheduled for release on November 4. On the same day, Nathan Lambert wrote that Kimi K3.1 Pro scored 55 on the Artificial Analysis Intelligence Index, ranking fourth. Tripo stated that its plugin is now natively supported in Kimi K3.1.
AI-generated from public reports
Latest development · Report
Kimi K3.1 Pro hands-on test: Why does quota consumption still spark controversy after the API price cut?Developments
4 confirmed developments
- Source:Moonshot AI · Kimi Blog
Nathan Lambert says a certain model scored higher than Fable 5.1 and Astra 6
Source:Nathan Lambert- Source:Tripo
- Source:IT Home · AI
Reports and discussion timeline
Browse reports and discussion by time. Their evidence levels remain distinct.
Oct 30
TMTPost · AGI
Kimi K3.1 Pro hands-on test: Why does quota consumption still spark controversy after the API price cut?
Kimi released K3.1 Pro on October 24, cutting the API input price from 20 yuan to 13 yuan per million tokens and the output price from 100 yuan to 68 yuan, but users questioned the cost-effectiveness due to rapid token consumption; on October 26, the company apologized, compensated quotas, and adjusted the model configuration. The author used a Pro subscription account to test five creative tasks, which consumed a total of about 25.48% of the monthly quota, with "The Peach Blossom Spring" at 6.91% the highest and the mechanical keyboard at 1.46% the lowest; consumption mainly came from Plugin entries and multiple rounds of revisions. The article also mentions the controversy over overseas bloggers being asked to hide paid partnership labels in promotions, and that former miHoYo international president Jin Wenyi joined Moonshot AI to oversee overseas expansion.
Oct 28
TMTPost · AGI
Top Model API Price Comparison: AI's "Hourly Wage" Is Surpassing Human Workers
GPT-6 Astra and Claude Fable 5.1 both cost $60 to process 1 million input tokens plus 1 million output tokens; Kimi K3.1 Pro costs 81 yuan, and its high-speed version 162 yuan—a single run can equal dozens of hours of wages for an ordinary employee. DeepSeek V4.1 Flash costs $0.15 per million input tokens and $0.60 per million output tokens during off-peak hours, creating a clear price gap through its low pricing. Last week, global AI large-model invocation volume reached 188 trillion tokens, with Chinese models accounting for 93.54 trillion tokens, exceeding the United States for 26 consecutive weeks.
Oct 27
t3nSelected
Moonshot AI Releases Open-Weight Model Kimi K3.1 Pro
Moonshot AI has released the open-weight model Kimi K3.1 Pro, a post-trained version of Kimi K3 that scores 65.7% on Terminal-Bench 4.0, higher than GPT-6 Astra and slightly lower than Claude Opus 5.5. Artificial Analysis calls it the strongest open-weight model currently available, with an intelligence index of 55 points, but notes that generation speed is slow and output is verbose; in evaluation it generated 150 million tokens, compared with a median of 81 million. Pricing is $2 per million input tokens and $10 per million output tokens. Moonshot AI plans to release the full weights by November 4 and says it has restricted the model's offensive cybersecurity capabilities.
36Kr · AI
Anthropic proposes folding sub-agents back into the main loop, responding to the controversy over Kimi K3.1 Pro quota consumption
Anthropic published the article "Building Agents: When to Fold Sub-Agents Back into the Main Loop," stating that a single agent with skills often outperforms multiple sub-agents dividing labor in quality, cost, and latency, with far lower token consumption than the latter, and open-sourced the single-agent-loop-kit tool. The article notes that multi-agent systems consume about 15 times the tokens of ordinary conversations, and converting sub-agents into Skills can cut over 80% of system prompts. After Moonshot AI's K3.1 Pro launched on October 24, multiple users reported excessively fast quota consumption, and the company said in the early hours of October 26 that it would reduce unnecessary sub-agent calls and output length.
36Kr · AI
After the Release of Kimi K3.1 Pro: A $50 Billion Valuation and an Apology Letter to Users Over Quotas
Kimi released K3.1 Pro, scoring 55 on the Artificial Analysis Intelligence Index—fourth globally and first among open-source models—and 67% on Terminal-Bench 4.0, surpassing GPT-6 Astra's 58.2%. Within 48 hours of launch, Kimi apologized for rate limiting and "unnecessary quota consumption," refunding 25% of monthly quotas. Running the full Artificial Analysis Intelligence Index test suite, K3.1 Pro outputs about 55 million tokens, 14% more than K3 and roughly 2.5 times that of GPT-6 Astra.
Phoenix Technology
Kimi K3.1 Pro quota depletes too quickly; Anthropic argues for folding sub-agents back into the main loop
After the launch of Moonshot AI's K3.1 Pro, multiple users reported that quotas were being consumed too quickly. The company apologized in the early hours of October 26 and compensated users with quota, while stating it would reduce unnecessary sub-agent calls and output length. The article cites Anthropic's piece "Building Agents: When to Fold Sub-Agents Back into the Main Loop," stating that token consumption in multi-agent systems is about 15 times that of ordinary conversations, and that converting sub-agents into Skills on demand can cut over 80% of system prompts. Anthropic has also open-sourced the single-agent-loop-kit tool, enabling models to execute tasks in a single-agent form only.
Oct 26
The Rundown AI
Moonshot Releases Kimi K3.1 Pro, Ranking Fourth on the Artificial Analysis Intelligence Index
Moonshot has launched Kimi K3.1 Pro, an upgraded version of July's K3. The company says it matches the strongest closed-source models on key benchmarks, and freely downloadable weights will be released by November 4. Artificial Analysis ranks it fourth on the Intelligence Index, ahead of OpenAI's GPT-6 Astra and behind only Anthropic's two 5.5-series models and Meta's Muse Wonder. Pricing is $2 per million input tokens and $10 per million output tokens, comparable to Sonnet 5.5, but its token consumption rate is higher than that of frontier rivals; AA also rated it the best publicly available cyber-defense model, with a score of 64% and no safety refusals.
gihyo.jpSelected
Moonshot AI releases the open-weight MoE model Kimi K3.1 Pro, and launches a 2 to 3 times faster…
On October 23, 2026, Moonshot AI released the multimodal MoE model Kimi K3.1 Pro, with a context length of 1 million tokens, 2.8T total parameters, and 104B activated parameters, while also launching Kimi K3.1 Pro Highspeed with a 2 to 3 times speed improvement. This version restructured the reinforcement learning process, surpassing GPT-6 Astra on Terminal-Bench 4.0 and slightly below Claude Opus 5.5, tying with GPT-6 Astra and Claude Opus 5.5 for SOTA on OfficeQA Pro v2, and reducing hallucinations in financial scenarios by 65%. Both models are now available on Kimi.ai, Kimi Work, Kimi Code, and the API, with open-source weights planned to be publicly released by November 4, 2026.
English text is being prepared.
Moonshot AI Releases Kimi K3.1 Pro, Strengthening 3D Modeling and Spatial Reasoning
Moonshot AI has released Kimi K3.1 Pro, strengthening 3D creation and spatial reasoning capabilities, and launched the 3D Make feature, which supports online preview and editing of 3D models, downloading files for 3D printing, or exporting as videos and Live Photos. The model is the first product in the K3.1 series, supports a 1 million Token context, and can write and run scripts for software such as Blender, SolidWorks, and Rhino to complete modeling. Official BenchCAD results show that, in a test subset consisting of 1,000 files, the voxel IoU after tool invocation was 92.7%, versus 72.8% without tools; the model is currently integrated into the Kimi web client, Kimi Work, Kimi Code, and the API, the Highspeed version outputs at roughly 2 to 3 times the speed of the standard version, and the full model weights are planned for release before November 4.
Caixin
Moonshot AI launches Kimi K3.1 Pro, a post-trained version of K3
In the early hours of October 24, Moonshot AI (Kimi) officially launched Kimi K3.1 Pro. This is the post-trained version of Kimi K3 and its most capable 3-trillion-parameter model to date, supporting a 1 million context window. Kimi says the model shows particularly notable improvements in coding, 3D creation, financial research and other long-horizon Agent capabilities. This is the first time Kimi has introduced a Pro version in its model versions, and K3.1 Pro is also the first model in the K3.1 series, leaving room for subsequent releases of cost-effective models.
Oct 25
Recode China AIFirst-party
China AI Weekly: DeepSeek's roughly $15 billion funding, Kimi K3.1 Pro release, and employment policy adjustments
The China AI Weekly Digest summarizes 306 China AI news items tracked from October 16 to 23, 2026. According to Reuters, DeepSeek's latest funding round raised about 100 billion yuan, with a pre-money valuation of 500 billion yuan; Tencent and CATL were the main investors. Moonshot AI released Kimi K3.1 Pro with a million-token context window and said the Highspeed version is two to three times faster. The General Office of the State Council issued AI employment policy guidelines, proposing 18 tasks covering new profession cultivation, AI training, and employment risk monitoring.
Sina Tech
whatmodel.sh: In October 2026, the performance gap between Chinese and US AI large models narrowed to 1 month
whatmodel.sh data shows that by October 2026, the performance gap between Chinese and US AI large models had narrowed to about 1 month. The latest models compared were DeepSeek V4.1 Flash-1017; if Kimi K3.1 Pro is included, the gap would be even smaller. When the DeepSeek V4 series was released at the end of April, a US congressional report claimed it lagged the United States by 8 months. Next, Zhipu GLM-5.5, Alibaba Qwen4 series, and DeepSeek V4.1 Pro are expected to continue raising the performance ceiling of domestic AI.
Huxiu
Behind the Kimi K3.1 Pro quota controversy: the stronger the model, the tighter the compute
Moonshot AI released Kimi K3.1 Pro on October 24, scoring 65.7% on Terminal-Bench 4.0, but users of the 199-yuan and 699-yuan plans complain that quotas are insufficient, and some developers estimate that the API value of the old plans is about 1.94 times that of the new plans. K3.1 Pro's per-million-Token input and output prices dropped from K3's 20 yuan and 100 yuan to 13 yuan and 68 yuan, while complex Agent tasks can consume millions of Tokens. A CAICT report shows that in June 2026, domestic daily average Tokens were about 500 trillion, nearly four times the figure in December 2025; the author believes that the compute shortage will still constrain independent model companies over the long term.
Oct 24
Huxiu
Kimi K3.1 Pro Released: Capabilities Improved, but Price Is 16 Times That of DeepSeek and MiniMax Flash
Moonshot AI released Kimi K3.1 Pro on October 24, retaining K3's 2.8 trillion parameters without redoing pretraining, and raised its Artificial Analysis composite intelligence index from about 44 to about 55 by increasing post-training compute and joint reinforcement learning across hundreds of domains. Its pricing is $2 and $10 per million input/output tokens, or $12 for 1 million tokens each, 16 times the $0.75 idle-time rate of MiniMax M3.1 Flash and DeepSeek V4.1 Flash-1017. The article also compares the three vendors' respective reinforcement in 3D modeling and financial research, full project development, and computer operation and cross-application workflows, and notes that some users report 3D tasks consume tokens so fast that they hit the quota limit.
English text is being prepared.Selected
Moonshot AI Releases Kimi K3.1 Pro: 3 Trillion Parameters, 1 Million Context, Coding Agent Benchmarks Surpass GPT-6 Astra
Moonshot AI officially launched K3.1 Pro, the first model in the Kimi K3.1 series. It is a 3-trillion-parameter model supporting a 1-million-token context, and a Highspeed version with roughly 2–3x the output speed is being released alongside it. On Terminal-Bench 4.0, K3.1 Pro scored 65.7%, surpassing GPT-6 Astra's 58.2% and coming close to Claude Opus 5.5's 66.4%; on OfficeQA Pro v2 it scored 64.4%, achieving the current best. Starting today, it is available via kimi.com, the Kimi App, Kimi Work, Kimi Code, and the Kimi API. The full model weights will be released by November 4. API input pricing is 1.3 yuan per million tokens (cache hit) and 13 yuan (cache miss), with output at 68 yuan.
ScienceNetSelected
Moonshot AI releases Kimi K3.1 Pro with 3-trillion-level parameters and a 1-million-token context
Moonshot AI has released Kimi K3.1 Pro, a new-generation open-source large model with 3-trillion-level parameters that supports a 1-million-token context window, retaining its title as the open-source model with the largest parameter scale in the world. The model is a post-trained version of Kimi K3, with a substantial increase in post-training compute, and it shows notable improvements in coding, 3D creation, financial research and long-horizon Agent capabilities; across multiple international benchmarks, its overall intelligence level is now approaching that of the world's leading closed-source models. The report also noted that OpenRouter statistics show global total AI large-model API calls from October 12 to 18 reached 164 trillion tokens, and that the weekly call volume of Chinese large models has exceeded that of the United States for 25 consecutive weeks.
Latent SpaceSelected
Moonshot AI releases Kimi K3.1 Pro: 2.8T total parameters, 104B activated parameters, weights scheduled to be opened on November 4
Moonshot AI releases Kimi K3.1 Pro, a multimodal MoE model with 2.8T total parameters, 104B activated parameters, and a 1 million token context window. The Standard and Highspeed versions are now available on the Kimi website, App, Kimi Work, Kimi Code and API. The company says Highspeed offers 2–3x faster output at twice the price of the Standard version. Artificial Analysis ranks it fourth on the Intelligence Index with a score of 55, behind Opus 5.5's 58; on Arena's Code Arena WebDev it ranks 8th with 1692 points, up nine places from K3. Standard pricing is $2 per million tokens for input, $10 for output, and $0.2 for cached input, one-third lower than K3; the weights have not yet been released, and the company promises to open them on November 4.
TripoFirst-party
Tripo plugin natively supports Kimi K3.1
Tripo reposted a demo of end-to-end modeling of Singapore Changi Airport in Blender using Kimi K3.1 Pro, and noted that the Tripo plugin is now natively supported in Kimi K3.1, making it convenient for creators to refine assets.
The Beijing NewsSelected
Moonshot AI releases open-source model Kimi K3.1 Pro, 3 trillion parameters, 1 million token context
Moonshot AI released a new-generation open-source model Kimi K3.1 Pro in the early hours of October 24, at a 3 trillion-level parameter scale, supporting a 1 million token context window, with full model weights to be released before November 4. The model increases compute investment in the reinforcement learning stage and improves capabilities in code writing, 3D creation, financial research, and long-horizon Agents. On safety, the team says its cyber offense and defense capabilities, though improved over the previous generation, still lag behind frontier closed-source models that have been publicly released for at least three months, and it explicitly will not release open weights that advance cyber offense and defense capabilities to the absolute frontier.
36Kr · AISelected
Kimi releases K3.1 Pro: 3 trillion parameters, million-token context, coding and Agent capabilities approaching Opus 5.5
On October 24, Kimi released Kimi K3.1 Pro, the first model in the K3.1 series. It has 3 trillion-level parameters and supports a million-token context, with a focus on strengthening coding, 3D creation, financial research, and long-horizon Agent capabilities, and it simultaneously launched a Highspeed version with output speed increased by about 2–3 times. On Terminal-Bench 4.0, K3.1 Pro scored 65.7%, surpassing GPT-6 Astra's 58.2% and within less than 1 percentage point of Claude Opus 5.5's 66.4%; in internal tests on financial scenarios, the single-item factual error rate dropped from 23.4% for K3 to 8.3%. The model is already available on kimi.com, the Kimi App, Kimi Work, and Kimi Code, and the full weights will be released before November 4. For the API, cached-hit input costs 1.3 yuan per million tokens, cache-miss input costs 13 yuan, and output costs 68 yuan; community feedback has centered on subscription quotas being consumed too quickly, and Artificial Analysis measured that it cumulatively output about 150 million tokens in the Intelligence Index test, higher than the model median of 87 million tokens.
Shao MengSelected
Moonshot AI Releases Kimi K3.1 Pro: A 3T-Class Open-Weight Model Strengthening Coding, 3D and Finance Agents
Moonshot AI has released Kimi K3.1 Pro, calling it the strongest 3T-class open-weight model, featuring a 1M-token context and multimodal capabilities. It simultaneously launched a Highspeed version that is about 2–3× faster, with full weights planned to be open-sourced before 2026-11-04. The model scores 65.7% on Terminal-Bench 4.0, above GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; it scores 92.7% on BenchCAD with tools and 64.4% on OfficeQA Pro v2. The open-weight version removes cyber-related data from both mid-training and RL, and its xdev-bench-hard score drops from 90.5% to 81.0%.
SiliconANGLESelected
Moonshot AI releases Kim K3.1 Pro flagship model with 3T parameter scale
Moonshot AI releases Kim K3.1 Pro, a post-training upgrade of Kimi K3 released in July; Kimi K3 consists of 896 neural networks with 3T total parameters, activating only 16 of them per inference. The new model scores 92.7% on the 3D modeling coding benchmark BenchCAD, 65.7% on Terminal-Bench 4.0, and surpasses Astra and Claude Opus 5.5 on OfficeQA Pro v2. The model is now live on Moonshot AI's paid cloud service, with weights planned to be open-sourced on November 4.
Moonshot AI · Kimi BlogFirst-partySelected
Moonshot AI releases Kimi K3.1 Pro: a 3T-class open-weight model with a 1 million token context
Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series and its strongest 3T-class open-weight model to date, with a 1 million token context window, alongside Kimi K3.1 Pro Highspeed, which is 2-3 times faster. The model is now available on Kimi.com, Kimi Work, Kimi Code and the Kimi API, with full model weights to be released on November 4. On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, higher than GPT-6 Astra's 58.2% and slightly below Claude Opus 5.5's 66.4%; on the internal benchmark Kimi Code Bench 3.0 Long-horizon it scores 61.2%, higher than K3's 55.95%.
CaixinSelected
Kimi Releases 3-Trillion-Parameter Model K3.1 Pro, Launching a Pro Version for the First Time
In the early hours of October 24, Moonshot AI (Kimi) officially launched Kimi K3.1 Pro, calling it its most capable 3-trillion-level parameter model to date, supporting a 1-million context window, with notable improvements in coding, 3D creation, financial research, and long-horizon Agent capabilities. Kimi K3.1 Pro is a post-trained version of Kimi K3; through enhanced post-training compute and technical optimization, it achieves comprehensive improvements across all domains from large-scale RL. This is the first time Kimi has introduced a Pro model in its model versions, and it is also the first model in the K3.1 series, leaving room for subsequent cost-effective models.
MarkTechPostSelected
Moonshot AI releases Kimi K3.1 Pro: 2.8T total parameters, 104B activated, 1M context
Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series, using a MoE architecture with 2.8T total parameters, 104B activated, and support for a 1M token context window. The model is now available on Kimi.ai, Kimi Work, Kimi Code, and the OpenAI-compatible Kimi API, with API pricing at $2 per million tokens for input and $10 for output, lower than K3's $3 and $15. Moonshot self-reports a Terminal-Bench 4.0 score of 65.7%, and SWE-Marathon 51.7 leads all models in its chart, but Terminal-Bench Science 0.1 is only 38.1, behind GPT-6 Astra's 68.1; open weights are expected to be released in November 2026.
Simon WillisonSelected
Moonshot AI's Kimi K3.1 Pro launches on API, weights to be opened on November 4
Kimi K3.1 Pro launches on the Kimi API today; weights are not yet open, and Moonshot AI promises to release the full model weights before November 4, 2026. Pricing is $2.00 per million input tokens and $10 per million output tokens, the same as GPT-6.1 Sol and half that of Claude Opus 5.5; a Highspeed version that is 2-3 times faster at double the price is also offered. Artificial Analysis gives Kimi a score of 55, higher than Claude Fable 5.1, GPT-6 Astra and Gemini 4 Argon at 53, and second only to Claude Opus 5.5 at 58. Simon Willison tested SVG generation via the Moonshot API at three reasoning effort levels: low, medium and max.
Rohan Paul
Moonshot weakened Kimi K3.1 Pro's hacking capabilities before open release
Before open release, Moonshot proactively weakened Kimi K3.1 Pro's cyberattack capabilities; the unweakened version had a 90.5% solve rate on its internal exploit task, while the released version was 81%. This result is still far higher than K3's 33.3%, but lower than DeepSeek V4.1-Flash's 85.7%. Moonshot said that exploit skills rise as post-training scale expands, so it removed cyber-related data from mid-training and reinforcement learning, and internal xdev-bench-hard pass@3 dropped from 90.5% to 81.0%.
Rohan PaulSelected
Moonshot AI Releases Kimi K3.1 Pro, Scoring 65.7% on Terminal-Bench 4.0
Moonshot AI has released Kimi K3.1 Pro, its first updated version, which scores 65.7% on Terminal-Bench 4.0—0.7 points lower than Claude Opus 5.5 and 7.5 points higher than GPT-6 Astra—while its average cost per task is lower than both. The model is a 3T-class open-weight MoE model with 2.8T total parameters and 104B active parameters, supports a 1M-token context and multimodality; in a tool-augmented hallucination stress test, its false-claim rate dropped from 23.4% in K3 to 8.3%. K3.1 Pro and the 2–3× faster Highspeed version are now available in the Kimi app and API, with full weights scheduled for release on November 4.
WallstreetcnSelected
Moonshot AI releases Kimi K3.1 Pro, 2.8 trillion parameters, coding agent benchmark surpasses GPT-6 Astra
Moonshot AI launches flagship model Kimi K3.1 Pro, with 2.8 trillion total parameters, adopting MoE architecture, 104 billion parameters activated each time, supporting a 1 million Token context window, and plans to open full model weights before November 4. The model scored 65.7% on the Terminal-Bench 4.0 coding agent test, higher than GPT-6 Astra's 58.2%, slightly lower than Claude Opus 5.5's 66.4%; on the BenchCAD benchmark it scored 92.7% with tool use. The standard version API output price is 68 yuan per million Tokens; the high-speed version is about 2 to 3 times faster than the standard version and priced at twice the price, and has been integrated into the Kimi official website, App, Kimi Work, Kimi Code and API.
OpenRouterFirst-partySelected
Kimi K3.1 Pro launches on OpenRouter, 1M context, 2.8T parameter MoE open-weight model
Kimi K3.1 Pro is now live on OpenRouter; it is a 1M-context multimodal open-weight model post-trained from Kimi K3, targeting long-horizon coding and agent tasks, priced at $2 per million input tokens and $10 per million output tokens. According to Kimi_Moonshot, the model is a 3T-class open-weight model with 2.8T total parameters and 104B activated parameters (MoE), excelling at full-project coding in Unreal, Unity, and Godot and at parametric 3D modeling in Blender and CAD, with financial modeling hallucinations reduced by 65%, and it is said to match the strongest closed-source models on coding, agent, and knowledge benchmarks. Kimi K3.1 Pro and the 2–3x faster K3.1 Pro Highspeed are now available on the Kimi platform, Kimi Work, Kimi Code, and Kimi API, with weights to be opened on November 4, 2026.
Nathan LambertSelected
Kimi K3.1 Pro Released, Intelligence Index Score of 55 Ranks Fourth
Kimi K3.1 Pro scored 55 on the Artificial Analysis Intelligence Index, ranking fourth, an 11-point improvement over Kimi K3's top score of 44. The model shows notable gains in agentic capabilities, scoring 1,846 Elo on GDPval-AA, 312 Elo higher than K3, and 67% on Terminal-Bench 4.0, above K3's 13%. The model has a 1M-token context window, 2.8T total parameters and 104B activated parameters, supports text and image inputs, and is priced at $2, $10, and $0.20 per 1M input, output, and cache-hit tokens respectively, 33% lower than K3; the weights have not yet been released, and Moonshot expects to release them shortly after launch.
AYi AI NotesSelected
Kimi K3.1 Pro Pricing Announced: $2 per Million Tokens Input, $10 Output
Kimi K3.1 Pro costs $2 per million tokens for input, $10 for output, and $0.20 for cache hits; the Highspeed version doubles both input and output prices. The author says its price is only two-thirds that of K3, and that it overtakes Opus 5.5 and GPT-6 Astra head-on on SWE Marathon.
Testing CatalogSelected
Kimi Releases K3.1 Pro: A 2.8T Total-Parameter, 104B Active MoE Open-Source Model
Kimi officially released Kimi K3.1 Pro, a 3T-class open-weight model built on a MoE architecture with 2.8T total parameters and 104B active parameters, supporting a 1M-token context and multimodality. The company says it performs comparably to leading models such as Opus 5.5 and GPT-6 Astra on benchmarks while costing significantly less, and that hallucinations in financial modeling scenarios dropped by 65%. Kimi K3.1 Pro and K3.1 Pro Highspeed, which is 2–3× faster, are now available on the Kimi platform, Kimi Work, Kimi Code, and the Kimi API, with open weights to be released on November 4.
Ayi AI NotesSelected
Kimi Releases 2.8T Flagship K3.1 Pro, Tops SWE Marathon with a Score of 51.7
Kimi has released its multimodal flagship model K3.1 Pro, with 2.8T total parameters and 104B active parameters (MoE), and a standard 1M-token context window. The author says it tops the SWE Marathon leaderboard with a score of 51.7, overtaking Opus 5.5 and GPT-6 Astra, and the company has also promised to open-source the full weights by November 4. K3.1 Pro and K3.1 Pro Highspeed, which is 2–3× faster, are now live on the Kimi web app, Kimi Work, Kimi Code and the Kimi API.
Unite.AISelected
Moonshot AI releases Kimi K3.1 Pro, the first model in the K3.1 series
Moonshot AI released Kimi K3.1 Pro on October 24, the first model in the K3.1 series, positioned as a 3T-class open-weight model supporting a 1 million token context window, and also launched Kimi K3.1 Pro Highspeed with 2 to 3 times faster speed. The company says K3.1 Pro scores 65.7% on Terminal-Bench 4.0, below Claude Opus 5.5's 66.4% and above GPT-6 Astra's 58.2%; in an internal hallucination stress test, its claim-level hallucination rate dropped from 23.4% in K3 to 8.3%. Both models are now available on Kimi.ai, mobile, the Kimi Work desktop app, Kimi Code and the Kimi API, and the full model weights will be released on November 4.
Nathan LambertSelected
Kimi K3.1 released under the name 3.1 Pro, with benchmark results leading current open-weight models
Kimi K3.1 was released under the name 3.1 Pro, and Nathan Lambert believes its benchmark scores are clearly the strongest among current open-weight models, provided the weights are released by November 4 as officially stated. The Kimi team explicitly discusses cyber risk and seeks to keep the open-weight cyber capability frontier in check, keeping it restrained relative to the closed-source frontier. K3.1's per-token price is lower than K3's, but some benchmarks in the blog show that it consumes significantly more tokens per answer, so the actual price may be similar.
Kimi.aiFirst-partySelected
Moonshot AI releases Kimi K3.1 Pro, calling it the strongest 3T-class model
Moonshot AI releases Kimi K3.1 Pro, which the company calls its most capable 3T-class model. The tweet only gives the model name and scale positioning, along with a video link, and does not disclose specific parameters, evaluation results, or release method.
Kimi.aiFirst-partySelected
Kimi releases K3.1 Pro open-weight model, 2.8T total parameters, 1M token context
Kimi releases K3.1 Pro, its strongest 3T-class open-weight model, using a MoE architecture with 2.8T total parameters and 104B activated parameters, supporting a 1M token context and multimodality. Officially it is claimed to match the strongest closed-source models on coding, agent, and knowledge benchmarks, and to support full-project game development in Unreal, Unity, and Godot as well as parametric 3D modeling in Blender and CAD, with hallucinations in financial scenarios reduced by 65%. K3.1 Pro and K3.1 Pro Highspeed, which is 2–3 times faster, are already live on the Kimi official website, Kimi Work, Kimi Code, and the Kimi API, with open weights planned for release on November 4, 2026.
Testing Catalog
Kimi K3.1 Pro is now available on the Kimi AI web and mobile platforms
Kimi K3.1 Pro is now available on the Kimi AI web and mobile platforms. This version is positioned as an all-around flagship model for complex tasks and professional work.
Oct 23
36Kr · AI
Kimi K3.1 suspected to appear on Arena, official video reveals model name
Kimi officially released a 24-second video, in which the model field of Kimi Code shows Kimi K3.1; previously an anonymous model Kibitz appeared on Arena and was suspected by netizens to be K3.1. Kibitz performed well in code tests such as 3D courtyard and pelican SVG, and a model-tracking account judged through Tokenizer features that it belongs to the Moonshot AI Kimi model family. Code commits in the SGLang repository show that K3.1 uses the same Tokenizer and 16 special Tokens as K3, and may add a new medium reasoning intensity tier.
TMTPost · AGI
Kimi K3.1 suspected to have appeared on Arena; official video reveals model name
Kimi officially released a 24-second video, in which the model field of Kimi Code shows Kimi K3.1; previously an anonymous model Kibitz appeared on Arena, which tracking accounts judged to have characteristics of the Moonshot AI model family. Commits in the SGLang repository show that K3.1 uses the same Tokenizer and 16 special Tokens as K3, and adds a medium reasoning intensity tier. K3 was previously priced at 3 USD per million input Tokens and 15 USD per million output Tokens, while Claude Opus 5.5 has dropped to 4 USD and 20 USD.
Leiphone
Kimi K3.1 leak: 3D generation cost is about 1/14 of GPT-6 Astra
Leiphone summarized the leaked information about Kimi K3.1: the text "Kimi K3.1" appeared in Kimi's official teaser; developers had previously found the kimi-k3-1 identifier in the Moonshot AI API configuration and the Kimi Code model list, and the anonymous arena model Kibitz is speculated to be this version. The article states that it performs outstandingly on 3D and front-end code tasks such as Three.js, that the token consumption for generating 3D code of equivalent complexity is far lower than that of top foreign models, with a cost of about 1/14 of GPT-6 Astra, and that a new "Medium" thinking tier has been added, expanding the thinking intensity from three levels to four. The article also mentions that it has obvious shortcomings in generating biological motion trajectories such as a running giraffe.
IT Home · Artificial Intelligence
Moonshot AI releases Kimi K3.1 teaser video, official launch date not yet announced
Moonshot AI has released a 24-second teaser video in which the model field in Kimi Code displays "Model: Kimi K3.1," but the official launch date, parameter scale, and pricing have not been announced. K3.1 had previously appeared in the API backend registry and the SGLang repository, supports a 1M-token context window, and adds a medium tier alongside the low, high, and max thinking-effort tiers. After its release, K3 was integrated into GitHub Copilot and AWS's Kiro, and the K3.1 teaser also centers on coding workflows.
X.PIN
Kimi teases new model K3.1, focusing on engineering tasks
Kimi has teased its next-generation model Kimi K3.1. According to the teaser, K3.1 doubles down on professional engineering tasks, with the goal of real-world execution rather than LLM capabilities. The author believes the key issue is price: Kimi is priced relatively high among Chinese models, which may slow user growth, and K3.1 needs to prove that its performance gains are enough to justify this cost.
Testing Catalog
Kimi K3.1 release officially teased
The release of Kimi K3.1 has been officially teased. The model was previously tested on Arena and is said to perform very well.
Full article from Artificial AnalysisFirst-partySelected
Kimi K3.1 Pro Released, Ranks 4th on Intelligence Index, Will Lead Open-Source Models Once Weights Are Released
Moonshot AI released the reasoning model Kimi K3.1 Pro, which scores 55 on the Artificial Analysis Intelligence Index, ranking 4th, an 11-point improvement over Kimi K3's 44 points, trailing only Claude Opus 5.5 (58), Claude Sonnet 5.5 (56), and Muse Wonder Full (56). Its agent capabilities improved notably: it scores 1,846 Elo on GDPval-AA, ranking 3rd; 67% on Terminal-Bench 4.0 (Kimi K3 was 13%); and AutomationBench-AA rose from 58% to 73%. The model has a 1M-token context window, 2.8T total parameters and 104B activated parameters, supports text and image input, and is priced at $2/10/0.20 per 1M input/output/cache-hit tokens, all three 33% lower than Kimi K3; the weights have not yet been released, and once released it will become the highest-scoring open-weight model, narrowing the gap between leading open-source and closed-source models from 12 points to 3 points.
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