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KIMI K3

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Headlines · 91

  1. Factory launches Droid 1.0 coding-agent harness

    Factory announced Droid 1.0, a model-independent coding-agent harness built around programmatic tool calling and an internal optimization loop. Factory says Droid outperformed native model harnesses on the same tasks with up to 20% greater efficiency, but the supplied report does not independently verify its benchmark, accuracy, or speed claims.

    Factory News · 🔥 0
  2. OpenRouter breaks down what a Claude Code session actually costs by model

    OpenRouter published session-cost data for Claude Code from its own platform telemetry covering the 30 days to October 18, 2026, reporting median costs of $0.31 for a 2-to-9-turn session on Claude Opus 5.5 and $0.16 on Claude Sonnet 5.5, with under a cent on Haiku 5.5. In a controlled bug-fixing test on a small invoicing library, all three Claude models fixed every bug, with median per-session costs of $0.098 on Opus 5.5, $0.062 on Sonnet 5.5 and $0.0052 on Haiku 5.5. The post also shows how to run Claude Code through OpenRouter's Ori Harness with per-session model selection, a cost-tracking statusline and credit limits or workspace budgets.

    OpenRouter Blog · 🔥 0
  3. Xiaomi MiMo Desktop upgrades membership with more quota and third-party models

    Xiaomi MiMo announced an immediate upgrade to MiMo Desktop membership benefits, increasing members’ overall usage quota without raising prices. Members can now select selected third-party models in addition to MiMo models, and IT之家 observed DeepSeek V4 Pro, GLM 5.3, Kimi K3, and Qwen 3.8 variants in the expanded list.

    IT之家 AI · 🔥 0
  4. Meta Releases HackBench for Detecting Reward Hacking in Research Agents

    Meta AI’s RAM research team released HackBench, a benchmark for detecting and locating reward hacking in autonomous research agents, according to DoNews. Its 169 audited trajectories include 41 native cases and 128 synthetic samples, with command-level labels for evaluating precision, recall and F1; Claude Opus 5 led the reported models with an F1 score of 64.88. Meta has published the trajectories and labels, while the full technical report is planned for arXiv.

    DoNews · 🔥 0
  5. Harness-Zero Moves Agent Harness Behavior Into a Distilled Model

    O’Reilly Radar reports that Haoran Ye and Peking University colleagues distilled an evolved agent harness into Qwen3.5-9B, then removed the harness and reviewer during testing. The distilled model’s average task success rose 21 points over its untrained predecessor, exceeding the improvement obtained by adding the full evolved harness, with evaluations covering spreadsheet editing, multi-app tool use and chemistry. The report says only generalizable harness behavior can move into the model; application-specific tools, data, users and environments still require a harness.

    O’Reilly Radar · 🔥 0
  6. Coding agents reproduce selected functions of biological AI tools

    The UK AI Security Institute reports that its ReproBAIT evaluation enabled Claude Opus 5, GPT-5.6 Sol, Kimi K3 and Qwen3.8-2.4T to reproduce selected functions of three biological AI tools; the simplest tools were reproduced within four hours and under $20 in compute. Claude Opus 5 reached 96.5% reproduction fidelity on ProteinMPNN, while no agent fully reproduced the most complex target, RFdiffusion3. AISI says the results are not a full reproduction and presents evidence of memorization as a directional signal rather than proof.

    UK AI Security Institute · 🔥 0
  7. Kimi K3.1 Pro Ranks Fourth, Then Apologizes for Quota Issues

    Moonshot AI released Kimi K3.1 Pro as an upgrade to its July K3 model, with free-to-download weights planned by Nov. 4. Artificial Analysis reportedly scored it 55 on its Intelligence Index, placing it fourth overall and first among open models, while some users reported slow performance and occasional wrong paths. Within 48 hours, Kimi apologized for rate limits and unnecessary quota consumption among paying users, offering compensation equal to one week of usage.

    36氪 人工智能 · 🔥 0
  8. A developer’s take on Kimi K3’s strengths, costs, and shortcomings

    In this practitioner review, stormzhangV says Kimi K3 paired with Kimi Code CLI delivered strong frontend work, capable long-running complex-task handling, and native video understanding in his tests. The author also reports comparatively high pricing, slower response speed, and a tendency to hallucinate, while ranking K3 among the world’s leading models but behind his preferred closed-source setups. He describes K3 as open-weight, with weights scheduled for release on July 27, but provides no independent benchmark data.

    掘金 · 人工智能 · 🔥 0
  9. AI Supernodes Shift Focus to Effective Performance

    AI infrastructure vendors are using tightly coupled supernodes to connect dozens or thousands of chips so they can work as a coordinated system. Examples cited by 36Kr, republishing AI前线, include NVIDIA’s GB200 NVL72, Huawei CloudMatrix 384 and Atlas 950 SuperPoD, and Inspur’s 元脑 SD200 Ultra. The commentary argues that adding chips raises theoretical compute but does not guarantee proportional performance gains because data movement, synchronization, memory access and scheduling can limit efficiency. It recommends determining the appropriate scale through workload testing based on the target model, precision, context length, concurrency, latency and deployment strategy. The report does not provide independent benchmarks comparing these products under equivalent workloads, and their advertised scales and performance claims are not directly comparable.

    36氪 人工智能 · 🔥 0
  10. Open vs. Closed AI: A Capital Allocation Thesis

    In a republished column, Xi Chunying argues that closed models may determine the ceiling of AI capability, open-weight models the pace of diffusion, and industrial applications where capital ultimately settles. The article uses Kimi K3 as a case study, citing its technical report for claims about model scale and capabilities, then explores three scenarios: closed-model oligopoly, long-term coexistence, and open-weight market dominance. Its forecasts about investment flows and profit migration are analytical scenarios rather than independently measured outcomes.

    虎嗅 AI · 🔥 0
  11. Cerebras Details Multi-Wafer Inference for Trillion-Parameter Models

    Cerebras has described how it maps the open-weight Kimi K3 model across 59 WSE-3T wafers, using 4-bit weights, an 8-bit KV cache, and a pipelined runtime. The company says activations move between wafers over RDMA Ethernet while multiple requests and micro-batches occupy different pipeline stages. Cerebras claims the multi-wafer system can run multi-trillion-parameter models at 1,000–2,000+ tokens per second, but no independent measurement was supplied.

    Hacker News 首页（100+ 分） · 🔥 0
  12. Moonshot AI launches Kimi K3.1 Pro with 1M-token context

    TestingCatalog reported that Moonshot AI launched Kimi K3.1 Pro, a 2.8T-parameter mixture-of-experts model with 104B active parameters and a 1M-token context window. It is available through Kimi’s web, mobile and API services, with open weights scheduled for November 4. TestingCatalog also reported that the model ranked fourth on the Artificial Analysis Intelligence Index, with a score of 55, and eighth on Arena’s WebDev leaderboard. These rankings were cited by TestingCatalog rather than confirmed directly from the original leaderboards.

    testingcatalog (X) · 🔥 0
  13. Artificial Analysis changes make benchmark scores harder to compare

    Artificial Analysis revised its benchmark methodology several times from v4.1 through v4.3.2, changing test weights, replacing or adding evaluations, retiring GPQA Diamond, and recalibrating GDPval Elo scores, according to its official changelog and methodology. Huxiu AI notes that Kimi K3 fell from 57 at its July evaluation to 44 on the current model page, while Kimi K3.1 Pro received 55 and was expected to lead the open-model ranking once weights were equalized. Because the service did not publish a crosswalk between old and new scores, results from different versions or capture dates should not be compared directly.

    虎嗅 AI · 🔥 0
  14. Kimi K3.1 Pro Ranks Eighth in WebDev Arena

    Arena.ai reported that Kimi K3.1 Pro ranked eighth in the Code Arena: WebDev with 1,692 points, nine places and 37 points above Kimi K3. It also said the model matched Claude Opus-5 (Max) at 70% lower cost. In Agent Arena, Kimi K3.1 Pro (Max) placed 11th after a reported 7.6% net improvement across more than 4,000 sessions, with a six-percentage-point gain over Kimi K3 (Max).

    arena (X) · 🔥 0
  15. GLM 5.3 and Kimi K3 Lead OpenRouter Security-Task Spending

    OpenRouter’s task-spend rankings placed GLM 5.3 and Kimi K3 at the top for spending share on security tasks. The ranking also identified Grok 4.7 as the leading closed-weight model in that category.

    OpenRouter (X) · 🔥 0
  16. Berget AI releases open-source per-request carbon accounting method

    Berget AI, Stockholm Environment Institute, WattTime and Klimatkollen released an MIT-licensed method for calculating the carbon emissions of individual AI requests. It follows the Green Software Foundation’s SCI for AI specification and uses average regional grid intensity plus hardware-manufacturing emissions. Berget AI says customers can track request-level emissions in its console. For one Kimi K3 request, the corrected estimate was 0.38 grams of CO₂e in Sweden and 3.2 grams in Texas, an 8.5-fold difference on identical hardware. Current estimates exclude training emissions and the water and energy footprint of chip manufacturing; marginal-emission estimates were planned for a later update.

    Hacker News · AI（100+ 分） · 🔥 0
  17. SWE-sweep Tests AI Agents Finding Hidden Bugs Without Issue Hints

    Researchers from Meta Super Intelligence Lab, Harvard University, Stanford and other institutions introduced SWE-sweep, a software-engineering benchmark built from 100 real open-source repositories containing 4,100 historical vulnerabilities. Unlike conventional issue-driven benchmarks, it gives coding agents only complete repositories and requires them to discover and repair hidden defects without descriptions, error messages or vulnerability locations. DoNews reported that successful repairs must turn a hidden test from failing to passing while preserving the repository’s full original test suite, with the initial leaderboard run uniformly on mini-SWE-agent.

    DoNews · 🔥 0
  18. Mirendil-Base trained by one researcher and AI agents

    Mirendil says one researcher used AI agents to pretrain the 100B–500B-parameter, code-focused Mirendil-Base from scratch in 3.5 months for less than $10 million, including agent token usage, data processing, and research experiments. The company says agents wrote more than 99% of the code, while the researcher set direction, evaluated results, and intervened at key points; training used public data without purchased datasets, distillation, or synthetic data. Mirendil’s tests reported that Mirendil-Base ranked second behind Kimi K3 on cross-entropy across seven held-out code repositories, ahead of Nemotron-550B-Base, GLM-5.3, and DeepSeek-V4-Pro-Base.

    36氪 人工智能 · 🔥 0
  19. Kimi K3 hands-on: strong 3D front-end coding, but quota burns fast

    A Juejin AI blogger published a hands-on test of Kimi K3, describing it as clearly stronger than the previous K2.7 on multi-modal front-end and 3D tasks, with a clean plan-then-verify workflow, but uneven across tasks. In the author's tests K3 handled a 3D garage page and a Super Mario recreation well, while it produced a flawed 3D billiards game and a less rich "cyberpunk Along the River During the Qingming Festival" scene than Fable 5. The author reports that access to K3 requires a 99-yuan-or-higher plan and that 1M context requires the 199-yuan tier, and that roughly five test pages exhausted a week's quota in under a day, with the first example alone using about 37% of the weekly allowance.

    掘金 · 人工智能 · 🔥 0
  20. OpenRouter details latest-model aliases for major AI families

    In a tip retweeted by Kenny Rogers, OpenRouter said developers can use aliases such as ~anthropic/claude-opus-latest to access the newest Claude Opus model, with existing code picking up a later version without changes. The tip says aliases are available for most model families and lists four current mappings: ~anthropic/claude-opus-latest to Claude Opus 5.5, ~openai/gpt-luna-latest to GPT-6 Luna, ~google/gemini-flash-latest to Gemini 3.8 Flash, and ~moonshotai/kimi-latest to Kimi K3.

    OpenRouter (X) · 🔥 0
  21. You.com launches Inference Router with third-party models and 99.9% uptime SLA

    You.com announced Inference Router, which it describes as enterprise-grade inference on demand, in a post reposted by Z.ai. The company said the router offers models including Kimi K3, GLM 5.3, DeepSeek V4.1 Flash, Qwen3.8 Flash, Muse Spark 1.3 and Mercury 3, with an uptime SLA of at least 99.9%. You.com also said the service includes US hosting and built-in web search; the details come from the vendor's own post and were not independently verified.

    Z.ai（智谱） · 🔥 0
  22. 12 AI Models Share a Conformity-Themed View of Humans

    R1’s test team asked 12 text models from 10 laboratories in three countries for their most controversial, unhedged take on humans three times. Every model gave a conformity-themed answer at least once, saying people prioritize group beliefs, belonging, status or personal stories over truth or principles; seven of the 12 gave that shared theme in all three answers. The study used no system prompt or retries and made 60 calls costing $1.54.

    Hacker News · AI（100+ 分） · 🔥 0
  23. TorchTitan Adds New Model Support, TitanRL and Scaling Improvements

    PyTorch reports that TorchTitan, a PyTorch-native platform for generative AI pretraining and post-training, now supports Muse Spark Open, Muse Glimmer, Qwen3-VL, Qwen3.5, Kimi K2.7, Kimi K3 and DeepSeek-V4 through full training-stack integrations. The update adds the TitanRL reinforcement-learning stack, asynchronous rollouts, FlexShard and Dist-MoE integrations, and distributed checkpointing improvements. PyTorch also reports internal benchmarks for these features, but the announcement is a single vendor source and does not provide an independent comparison with other training frameworks.

    PyTorch Blog · 🔥 0
  24. 17-year-old ex-Kimi researcher Nathan Chen reportedly joins Discovery Loop

    On October 20 the anonymous account Arfur Grok, which tracks Silicon Valley AI talent moves, reported that 17-year-old researcher Chen Guangyu (Nathan Chen) had joined Discovery Loop, the startup founded by former Google researchers including Jeff Dean. Chen had announced on October 15 that he was leaving Moonshot AI, where he worked on the Kimi models, and he publicly denied speculation that he was joining Anthropic. Neither Chen nor Discovery Loop has confirmed the move, and Discovery Loop said it does not comment on personnel matters. Founded in August 2026 by four former Google researchers, Discovery Loop has released no public product and is reportedly seeking funding at about a $50 billion valuation, with the goal of using AI to propose hypotheses, design and run experiments and iterate on results.

    InfoQ 中文 AI&大模型 · 🔥 0
  25. Qwen3.8-Flash-Next runs close behind Claude Opus 5.5 in a six-way coding benchmark

    A Reddit user (deepu105, r/LocalLLaMA) ran a controlled comparison on one real Rust feature from the LlamaStash roadmap, testing six model-harness-effort combinations twice for 12 pull requests; Flash-Next ran locally on a Strix Halo laptop (Flow Z13) via Halogen at roughly Q4 quantization, while Opus 5.5 ran through Claude Code. Claude Code with Opus 5.5 at xhigh effort scored 6.94 of 8 (87%) in 43.5 minutes at $17.78 of API cost, while Claude Code with Flash-Next at medium scored 5.5 of 8 (69%) in 45.3 minutes; the author reports Opus averaged 82% on code quality versus 68% for Claude Code + Flash-Next and 66% for Pi + Flash-Next, with the gap concentrated in edge cases, tests and documentation. All 12 runs finished and all 12 PRs passed the end-to-end test, and the author notes the models are not in the same league since Flash-Next ran quantized on a 70W-capped laptop.

    r/LocalLLaMA · 🔥 0
  26. Column says AI office race hinges on foundation models, channels and industry depth

    A column by the WeChat account Naojiti, republished by Huxiu AI and TMTPost, argues that China's AI office competition is being fought on three fronts: foundation models, marketing and channels, and industry-specific workflows. It says Tencent announced that WorkBuddy's built-in models now support Kimi K3, after previously offering Hunyuan, DeepSeek, GLM and MiniMax, and that Kimi K3's context window reaches up to one million tokens. It also cites an AICPB July 2026 desktop ranking with WorkBuddy first at 11.15 million monthly active users and Baidu Dazi second at 6.74 million, and describes Huawei Cloud's upgraded OfficeAce with more than 20 preset expert agents plus expanded finance skills.

    虎嗅 AI · 🔥 0
  27. DeepSeek-V4.1-Flash tops Code Arena WebDev open-source leaderboard

    Arena said on October 19 that DeepSeek-V4.1-Flash-1017 (Max), released October 17, scored 1670 on the Code Arena: WebDev leaderboard to become the highest-ranked open-source model, ahead of Kimi K3 (Max) and other open models and also ahead of closed models including Muse Spark 1.3 (Max), Claude Opus 5 (High) and Grok 4.7 (xHigh), trailing GPT-6 Sol (Max) by 12 points. The report says the model scored above GPT-6 Astra, GPT-6.1 Sol and Claude Opus 5 on the DeepSWE v1.1 software engineering test, with average cost per task down about 60% from the previous version; peak-hour pricing is 2 yuan per million input tokens on a cache miss and 8 yuan for output, falling to 0.04 yuan for cached input. DeepSeek says the model supports "wish-style programming" that generates games and 3D modeling in one click, and one netizen's test reported faster generation than GPT-6.1's ultra-fast mode.

    极客公园 · 🔥 0
  28. DeepSeek-V4.1-Flash-1017 tops Arena Code Arena WebDev and Agent Arena open rankings

    According to 智东西, Arena announced on October 19 that DeepSeek-V4.1-Flash-1017 (Max) became the highest-ranked open model on its Code Arena: WebDev leaderboard. The model scored 1670 points, ahead of Kimi K3 (Max) and Mimo-V2.6 Flash and also ahead of closed models including Muse Spark 1.3, Claude Opus 5 and Grok 4.7, while finishing 12 points behind GPT-6 Sol (Max). IT之家 reported on October 20 that Arena also ranked the model first among open models and 11th overall on Agent Arena; IT之家 cited 7,498 real usage sessions and a 5.13% net improvement, while an Arena post cited a 5.1% net improvement across more than 7,500 sessions. IT之家 put median cost per task at about $0.18–0.19, while the Arena post cited $0.20; Arena listed the model on its performance-versus-cost Pareto frontier. The model was released on October 17. DeepSeek says it supports “wish-style programming” that can generate games and 3D models in one click, and 智东西 reported that its average cost per task on the DeepSWE v1.1 software engineering test fell about 60% from the previous version.

    智东西 · 🔥 0
  29. Kimi K3 reportedly pulled from sale; developer tests its code-editing on a 3D pool game

    A Juejin author says he was testing Kimi K3 one day and the next day "heard" it had been taken off sale, adding that GLM 5.2 has stayed in a purchase-limited state since becoming popular. Neither claim is backed by vendor confirmation, and the author himself frames the K3 halt as hearsay. The bulk of the post is a hands-on test: over 12 rounds of dialogue he had K3 build a networked 3D billiards game using Three.js and hand-written physics; the first-round output had serious modeling problems (no pockets cut into the table) and erratic camera control, and only after many revisions did it reach a playable state. He rates K3 as broadly top-tier among domestic models with good scene modeling and summary documentation, but criticizes the poor first draft, long iteration, heavy token use and weak autonomous planning.

    掘金 · 人工智能 · 🔥 0
  30. Tencent reportedly tests “Dongleya” AI learning community

    IT之家 reported, citing user feedback, that Tencent is beta-testing “懂了鸭,” an AI interactive-content community for children and teenagers. The website and WeChat mini program reportedly offer limited testing to parents with children and frontline teachers. Its built-in AI agents can reportedly turn natural-language prompts into 3D demonstrations, dynamic lessons, interactive picture books, lightweight games and memory cards without requiring programming skills. Users can browse community works and adapt others’ creations. IT之家 also said the model list includes DeepSeek-V4.1-Flash, DeepSeek V4 Pro, DeepSeek V4 Flash, Hy3, Hy4 preview, MiniMax M3, Kimi K3 and GLM-5.3. The supplied reports do not include direct confirmation from Tencent, and they describe the test’s access arrangements differently.

    DoNews · 🔥 0

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kimi-k3-in-c• Pinned

kimi-k3-in-c: Kimi K3 inference in portable C99, with no BLAS, framework, or GPU required.

What problem it solves Running the Kimi K3 model normally presents a storage and memory barrier because its checkpoint is very large. This project provides a portable C99 inference engine that streams model data so the complete model can run on a CPU without requiring GPU infrastructure or a deep-learning framework. Key features Portable C99 inference — The engine is implemented in portable C99 an…

github.comhttps://github.com/FareedKhan-dev/kimi-k3-in-c